# Cross-position Activity Recognition with Stratified Transfer Learning

arXiv: https://arxiv.org/abs/1806.09776 · HTML: https://mlsanity.com/paper/1806.09776/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals](https://mlsanity.com/paper/2107.12626/) (2021)
- [Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI](https://mlsanity.com/paper/2105.12497/) (2021)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity](https://mlsanity.com/paper/2306.13735/) (2023)
- [Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review](https://mlsanity.com/paper/2403.15422/) (2024)
- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Cross-user activity recognition via temporal relation optimal transport](https://mlsanity.com/paper/2403.15423/) (2024)
- [Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey](https://mlsanity.com/paper/2304.06489/) (2023)
- [Activity Classification Using Unsupervised Domain Transfer from Body Worn Sensors](https://mlsanity.com/paper/2304.10643/) (2023)
- [Learning Alternative Ways of Performing a Task](https://mlsanity.com/paper/2404.02579/) (2024)
- [VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition](https://mlsanity.com/paper/2505.04907/) (2025)

## Abstract (arXiv)

Human activity recognition aims to recognize the activities of daily living by utilizing the sensors on different body parts. However, when the labeled data from a certain body position (i.e. target domain) is missing, how to leverage the data from other positions (i.e. source domain) to help learn the activity labels of this position? When there are several source domains available, it is often difficult to select the most similar source domain to the target domain. With the selected source domain, we need to perform accurate knowledge transfer between domains. Existing methods only learn the global distance between domains while ignoring the local property. In this paper, we propose a textitStratified Transfer Learning (STL) framework to perform both source domain selection and knowledge transfer. STL is based on our proposed textitStratified distance to capture the local property of domains. STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer. Extensive experiments on three public activity recognition datasets demonstrate the superiority of STL. Furthermore, we extensively investigate the performance of transfer learning across different degrees of similarities and activity levels between domains. We also discuss the potential applications of STL in other fields of pervasive computing for future research.