# SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition

arXiv: https://arxiv.org/abs/2212.00724 · HTML: https://mlsanity.com/paper/2212.00724/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization](https://mlsanity.com/paper/2112.00463/) (2021)
- [Partial Adversarial Domain Adaptation](https://mlsanity.com/paper/1808.04205/) (2018)
- [Generalized Inner Loop Meta-Learning](https://mlsanity.com/paper/1910.01727/) (2019)
- [Importance Weighted Adversarial Nets for Partial Domain Adaptation](https://mlsanity.com/paper/1803.09210/) (2018)
- [Learning to Transfer Examples for Partial Domain Adaptation](https://mlsanity.com/paper/1903.12230/) (2019)
- [Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data](https://mlsanity.com/paper/2005.10996/) (2020)
- [Beyond Sharing Weights for Deep Domain Adaptation](https://mlsanity.com/paper/1603.06432/) (2016)
- [Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks](https://mlsanity.com/paper/1903.12489/) (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)
- [Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation](https://mlsanity.com/paper/2204.06187/) (2022)

## Built on by (most cited first, 21 of 21)

- [UniMTS: Unified Pre-training for Motion Time Series](https://mlsanity.com/paper/2410.19818/) (2024)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition](https://mlsanity.com/paper/2403.15444/) (2024)
- [MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking](https://mlsanity.com/paper/2505.22769/) (2025)
- [Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey](https://mlsanity.com/paper/2304.06489/) (2023)
- [SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning](https://mlsanity.com/paper/2405.00712/) (2024)
- [C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2407.16803/) (2024)
- [HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets](https://mlsanity.com/paper/2503.13542/) (2025)
- [Multi-scale hypergraph meets LLMs: Aligning large language models for time series analysis](https://mlsanity.com/paper/2602.04369/) (2026)
- [BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition](https://mlsanity.com/paper/2605.08296/) (2026)
- [A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition](https://mlsanity.com/paper/2404.15366/) (2024)
- [Elevator, Escalator, or Neither? Classifying Conveyor State Using Smartphone under Arbitrary Pedestrian Behavior](https://mlsanity.com/paper/2405.03218/) (2024)
- [Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment](https://mlsanity.com/paper/2410.17489/) (2024)
- [Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation](https://mlsanity.com/paper/2504.14994/) (2025)
- [Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition](https://mlsanity.com/paper/2505.06301/) (2025)
- [Graph-Based Adversarial Domain Generalization with Anatomical Correlation Knowledge for Cross-User Human Activity Recognition](https://mlsanity.com/paper/2506.01962/) (2025)
- [HEDN: A Hard-Easy Dual Network with Source Reliability Assessment for Cross-Subject EEG Emotion Recognition](https://mlsanity.com/paper/2511.06782/) (2025)
- [Cross-View Cross-Modal Unsupervised Domain Adaptation for Driver Monitoring System](https://mlsanity.com/paper/2511.12196/) (2025)
- [Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition](https://mlsanity.com/paper/2603.16043/) (2026)
- [GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery](https://mlsanity.com/paper/2605.22086/) (2026)
- [Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition](https://mlsanity.com/paper/2609.16768/) (2026)

## Abstract (arXiv)

In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity. Existing UDA models usually align samples across domains without differentiation, which ignores the difference among samples. In this paper, we propose an unsupervised domain adaptation model with sample weight learning (SWL-Adapt) for cross-user WHAR. SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network. We introduce the meta-optimization based update rule to learn this network end-to-end, which is guided by meta-classification loss on the selected pseudo-labeled target samples. Therefore, this network can fit a weighting function according to the cross-user WHAR task at hand, which is superior to existing sample differentiation rules fixed for special scenarios. Extensive experiments on three public WHAR datasets demonstrate that SWL-Adapt achieves the state-of-the-art performance on the cross-user WHAR task, outperforming the best baseline by an average of 3.1% and 5.3% in accuracy and macro F1 score, respectively.