# Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks

arXiv: https://arxiv.org/abs/2012.04479 · HTML: https://mlsanity.com/paper/2012.04479/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors](https://mlsanity.com/paper/2210.08394/) (2022)
- [Label-efficient Time Series Representation Learning: A Review](https://mlsanity.com/paper/2302.06433/) (2023)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition](https://mlsanity.com/paper/2310.14390/) (2023)
- [Self-supervised Assisted Active Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2205.07021/) (2022)
- [Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression](https://mlsanity.com/paper/2404.15311/) (2024)
- [Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data](https://mlsanity.com/paper/2304.06471/) (2023)
- [Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks](https://mlsanity.com/paper/2308.06467/) (2023)
- [Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing](https://mlsanity.com/paper/2408.03480/) (2024)
- [Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification](https://mlsanity.com/paper/2409.20005/) (2024)
- [EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis](https://mlsanity.com/paper/2208.11743/) (2022)
- [EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures](https://mlsanity.com/paper/2408.03449/) (2024)
- [Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study](https://mlsanity.com/paper/2408.03472/) (2024)
- [Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data](https://mlsanity.com/paper/2408.03478/) (2024)

## Abstract (arXiv)

Human activity recognition (HAR) research has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this approach if their activity patterns are different than those in the training data. At the same time, training from scratch for new users is not feasible for mobile applications due to the high computational cost and training time. To address this issue, we propose a HAR transfer learning framework with two components. First, a representational analysis reveals common features that can transfer across users and user-specific features that need to be customized. Using this insight, we transfer the reusable portion of the offline classifier to new users and fine-tune only the rest. Our experiments with five datasets show up to 43% accuracy improvement and 66% training time reduction when compared to the baseline without using transfer learning. Furthermore, measurements on the Nvidia Jetson Xavier-NX hardware platform reveal that the power and energy consumption decrease by 43% and 68%, respectively, while achieving the same or higher accuracy as training from scratch.