# Scaling laws in wearable human activity recognition

arXiv: https://arxiv.org/abs/2502.03364 · HTML: https://mlsanity.com/paper/2502.03364/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Large-scale Training of Foundation Models for Wearable Biosignals](https://mlsanity.com/paper/2312.05409/) (2023)
- [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)
- [Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches](https://mlsanity.com/paper/1609.06354/) (2016)
- [Improving Deep Learning for HAR with shallow LSTMs](https://mlsanity.com/paper/2108.00702/) (2021)
- [Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey](https://mlsanity.com/paper/2004.08821/) (2020)
- [CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition](https://mlsanity.com/paper/2402.19229/) (2024)
- [Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition](https://mlsanity.com/paper/2312.02185/) (2023)

## Built on by (most cited first, 4 of 4)

- [Inertia-1: An Open Exploration of Wearable Motion Foundation Models](https://mlsanity.com/paper/2607.06617/) (2026)
- [Experience Paper: Adopting Activity Recognition in On-demand Food Delivery Business](https://mlsanity.com/paper/2509.24303/) (2025)
- [Bridging Generalization and Personalization in Human Activity Recognition via On-Device Few-Shot Learning](https://mlsanity.com/paper/2508.15413/) (2025)
- [MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition](https://mlsanity.com/paper/2610.10245/) (2026)

## Abstract (arXiv)

Many deep architectures and self-supervised pre-training techniques have been proposed for human activity recognition (HAR) from wearable multimodal sensors. Scaling laws have the potential to help move towards more principled design by linking model capacity with pre-training data volume. Yet, scaling laws have not been established for HAR to the same extent as in language and vision. By conducting an exhaustive grid search on both amount of pre-training data and Transformer architectures, we establish the first known scaling laws for HAR. We show that pre-training loss scales with a power law relationship to amount of data and parameter count and that increasing the number of users in a dataset results in a steeper improvement in performance than increasing data per user, indicating that diversity of pre-training data is important, which contrasts to some previously reported findings in self-supervised HAR. We show that these scaling laws translate to downstream performance improvements on three HAR benchmark datasets of postures, modes of locomotion and activities of daily living: UCI HAR and WISDM Phone and WISDM Watch. Finally, we suggest some previously published works should be revisited in light of these scaling laws with more adequate model capacities.