# Cross-domain Activity Recognition via Substructural Optimal Transport arXiv: https://arxiv.org/abs/2102.03353 ยท HTML: https://mlsanity.com/paper/2102.03353/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 66 references that are in the map. - First posted on arXiv 29 Jan 2021. ## Builds on (most cited first, 19 of 19) - [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013) - [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018) - [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015) - [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018) - [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015) - [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018) - [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017) - [Theoretical Analysis of Domain Adaptation with Optimal Transport](https://mlsanity.com/paper/1610.04420/) (2016) - [Visual Domain Adaptation with Manifold Embedded Distribution Alignment](https://mlsanity.com/paper/1807.07258/) (2018) - [Deep Visual Domain Adaptation](https://mlsanity.com/paper/2012.14176/) (2020) - [Stratified Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1801.00820/) (2017) - [Transfer Learning with Dynamic Distribution Adaptation](https://mlsanity.com/paper/1909.08531/) (2019) - [Balanced Distribution Adaptation for Transfer Learning](https://mlsanity.com/paper/1807.00516/) (2018) - [Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors](https://mlsanity.com/paper/1708.08989/) (2017) - [Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model](https://mlsanity.com/paper/2011.11499/) (2020) - [Unsupervised Domain Adaptation using Regularized Hyper-graph Matching](https://mlsanity.com/paper/1805.08874/) (2018) - [Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis](https://mlsanity.com/paper/2006.05602/) (2020) - [Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders](https://mlsanity.com/paper/1610.03761/) (2016) - [Sample-to-Sample Correspondence for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1805.00355/) (2018) ## Built on by (most cited first, 21 of 21) - [FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning](https://mlsanity.com/paper/2302.13485/) (2023) - [MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare](https://mlsanity.com/paper/2206.08516/) (2022) - [Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition](https://mlsanity.com/paper/2206.06629/) (2022) - [Personalized Federated Learning with Adaptive Batchnorm for Healthcare](https://mlsanity.com/paper/2112.00734/) (2021) - [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024) - [Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning](https://mlsanity.com/paper/2306.04641/) (2023) - [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024) - [MHMS: Multimodal Hierarchical Multimedia Summarization](https://mlsanity.com/paper/2204.03734/) (2022) - [Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment](https://mlsanity.com/paper/2210.04722/) (2022) - [Cross-user activity recognition via temporal relation optimal transport](https://mlsanity.com/paper/2403.15423/) (2024) - [Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learning](https://mlsanity.com/paper/2412.13594/) (2024) - [Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices](https://mlsanity.com/paper/2402.03714/) (2024) - [Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition](https://mlsanity.com/paper/2403.14682/) (2024) - [Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition](https://mlsanity.com/paper/2403.17958/) (2024) - [Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing](https://mlsanity.com/paper/2503.10663/) (2025) - [Deconfounding Causal Inference through Two-Branch Framework with Early-Forking for Sensor-Based Cross-Domain Activity Recognition](https://mlsanity.com/paper/2507.03898/) (2025) - [SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay](https://mlsanity.com/paper/2404.15305/) (2024) - [Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers](https://mlsanity.com/paper/2303.07597/) (2023) - [Safe Screening for Unbalanced Optimal Transport](https://mlsanity.com/paper/2307.00247/) (2023) - [Cross-user activity recognition using deep domain adaptation with temporal relation information](https://mlsanity.com/paper/2403.15424/) (2024) - [Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning](https://mlsanity.com/paper/2408.03353/) (2024) ## Abstract (arXiv) It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Domain adaptation is a promising approach for cross-domain activity recognition. Existing methods mainly focus on adapting cross-domain representations via domain-level, class-level, or sample-level distribution matching. However, they might fail to capture the fine-grained locality information in activity data. The domain- and class-level matching are too coarse that may result in under-adaptation, while sample-level matching may be affected by the noise seriously and eventually cause over-adaptation. In this paper, we propose substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer. Based on SSDA, we propose an optimal transport-based implementation, Substructural Optimal Transport (SOT), for cross-domain HAR. We obtain the substructures of activities via clustering methods and seeks the coupling of the weighted substructures between different domains. We conduct comprehensive experiments on four public activity recognition datasets (i.e. UCI-DSADS, UCI-HAR, USC-HAD, PAMAP2), which demonstrates that SOT significantly outperforms other state-of-the-art methods w.r.t classification accuracy (9%+ improvement). In addition, our mehtod is 5x faster than traditional OT-based DA methods with the same hyper-parameters.