# Transfer Learning for Activity Recognition in Mobile Health

arXiv: https://arxiv.org/abs/2007.06062 · HTML: https://mlsanity.com/paper/2007.06062/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

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

- [Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets](https://mlsanity.com/paper/2205.13607/) (2022)
- [Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets](https://mlsanity.com/paper/2107.06097/) (2021)

## Abstract (arXiv)

While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation. Aiming to address these challenges, we propose a transfer learning framework, TransFall, for sensor-based activity recognition. TransFall's design contains a two-tier data transformation, a label estimation layer, and a model generation layer to recognize activities for the new scenario. We validate TransFall analytically and empirically.