# Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review

arXiv: https://arxiv.org/abs/2403.15422 · HTML: https://mlsanity.com/paper/2403.15422/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 109 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016)
- [Protecting Sensory Data against Sensitive Inferences](https://mlsanity.com/paper/1802.07802/) (2018)
- [Stratified Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1801.00820/) (2017)
- [Deep Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1807.07963/) (2018)
- [Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization](https://mlsanity.com/paper/2005.12178/) (2020)
- [Cross-position Activity Recognition with Stratified Transfer Learning](https://mlsanity.com/paper/1806.09776/) (2018)
- [Few-Shot Learning-Based Human Activity Recognition](https://mlsanity.com/paper/1903.10416/) (2019)
- [Personalized Human Activity Recognition Using Convolutional Neural Networks](https://mlsanity.com/paper/1801.08252/) (2018)
- [Distributionally Robust Semi-Supervised Learning for People-Centric Sensing](https://mlsanity.com/paper/1811.05299/) (2018)
- [Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors](https://mlsanity.com/paper/1808.01358/) (2018)

## Built on by (most cited first, 9 of 9)

- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets](https://mlsanity.com/paper/2503.13542/) (2025)
- [Fairness in Machine Learning-based Hand Load Estimation: A Case Study on Load Carriage Tasks](https://mlsanity.com/paper/2504.05610/) (2025)
- [Ethical Fairness in Ubiquitous Health Sensing without Known Attributes](https://mlsanity.com/paper/2603.13373/) (2026)
- [Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition](https://mlsanity.com/paper/2505.06301/) (2025)
- [ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling](https://mlsanity.com/paper/2505.17987/) (2025)
- [Graph-Based Adversarial Domain Generalization with Anatomical Correlation Knowledge for Cross-User Human Activity Recognition](https://mlsanity.com/paper/2506.01962/) (2025)
- [Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling](https://mlsanity.com/paper/2508.21785/) (2025)
- [Reinforcement Learning Driven Generalizable Feature Representation for Cross-User Activity Recognition](https://mlsanity.com/paper/2509.01031/) (2025)

## Abstract (arXiv)

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with less annotated data. This review investigates how machine learning addresses data heterogeneity in HAR, by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.