{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:28:13Z","timestamp":1774416493887,"version":"3.50.1"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2025-24803185"],"award-info":[{"award-number":["RS-2025-24803185"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2019-II190421"],"award-info":[{"award-number":["RS-2019-II190421"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2025-25442569"],"award-info":[{"award-number":["RS-2025-25442569"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["IITP-2025-RS-2024-00360227"],"award-info":[{"award-number":["IITP-2025-RS-2024-00360227"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["IITP-2026-RS-2020-II201821"],"award-info":[{"award-number":["IITP-2026-RS-2020-II201821"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00436936"],"award-info":[{"award-number":["RS-2024-00436936"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006465","name":"Korea Creative Content Agency","doi-asserted-by":"publisher","award":["RS-2024-00333068"],"award-info":[{"award-number":["RS-2024-00333068"]}],"id":[{"id":"10.13039\/501100006465","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.eswa.2026.131585","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:07:27Z","timestamp":1772122047000},"page":"131585","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Harnessing spatial dependency for domain generalization in multivariate time-series sensor data"],"prefix":"10.1016","volume":"317","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2480-3994","authenticated-orcid":false,"given":"Jaehyun","family":"Bae","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6554-2391","authenticated-orcid":false,"given":"Heesoo","family":"Jung","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-5806","authenticated-orcid":false,"given":"Hogun","family":"Park","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.131585_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103079","article-title":"Tscmamba: Mamba meets multi-view learning for time series classification","volume":"120","author":"Ahamed","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.eswa.2026.131585_bib0002","series-title":"Proceedings of the european symposium on artificial neural networks","article-title":"A public domain dataset for human activity recognition using smartphones","author":"Anguita","year":"2013"},{"key":"10.1016\/j.eswa.2026.131585_bib0003","unstructured":"Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv: 1803.01271."},{"key":"10.1016\/j.eswa.2026.131585_bib0004","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin","year":"2006","journal-title":"Journal of Machine Learning Research"},{"issue":"6","key":"10.1016\/j.eswa.2026.131585_bib0005","doi-asserted-by":"crossref","first-page":"2927","DOI":"10.3390\/s23062927","article-title":"Reducing noise, artifacts and interference in single-channel EMG signals: A review","volume":"23","author":"Boyer","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2026.131585_bib0006","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"11141","article-title":"Msgnet: Learning multi-scale inter-series correlations for multivariate time series forecasting","volume":"vol. 38","author":"Cai","year":"2024"},{"key":"10.1016\/j.eswa.2026.131585_bib0007","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.compbiomed.2018.08.020","article-title":"Position-independent gesture recognition using sEMG signals via canonical correlation analysis","volume":"103","author":"Cheng","year":"2018","journal-title":"Computers in Biology and Medicine"},{"issue":"5","key":"10.1016\/j.eswa.2026.131585_bib0008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3643035","article-title":"Domain generalization in time series forecasting","volume":"18","author":"Deng","year":"2024","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.1016\/j.eswa.2026.131585_bib0009","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.neunet.2022.07.032","article-title":"Multivariate time-series classification with hierarchical variational graph pooling","volume":"154","author":"Duan","year":"2022","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2026.131585_bib0010","series-title":"Proceedings of the 41st international conference on machine learning (ICML)","first-page":"12409","article-title":"Tslanet: Rethinking transformers for time series representation learning","volume":"vol. 235","author":"Eldele","year":"2024"},{"key":"10.1016\/j.eswa.2026.131585_bib0011","series-title":"Proceedings of the 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP)","article-title":"Compressed sensing with unknown sensor permutation","author":"Emiya","year":"2014"},{"key":"10.1016\/j.eswa.2026.131585_bib0012","doi-asserted-by":"crossref","first-page":"24934","DOI":"10.52202\/068431-1808","article-title":"Debiasing graph neural networks via learning disentangled causal substructure","volume":"35","author":"Fan","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"10.1016\/j.eswa.2026.131585_bib0013","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TNSRE.2014.2305111","article-title":"The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges","volume":"22","author":"Farina","year":"2014","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"10.1016\/j.eswa.2026.131585_bib0014","series-title":"Proceedings of the international conference on machine learning","article-title":"Domain adaptation for large-scale sentiment classification: A deep learning approach","author":"Glorot","year":"2011"},{"key":"10.1016\/j.eswa.2026.131585_bib0015","series-title":"Proceedings of the international conference on machine learning","first-page":"12746","article-title":"Domain adaptation for time series under feature and label shifts","author":"He","year":"2023"},{"issue":"8","key":"10.1016\/j.eswa.2026.131585_bib0016","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"10.1016\/j.eswa.2026.131585_bib0017","series-title":"Proceedings of the international conference on robotics and automation","article-title":"Causal-based time series domain generalization for vehicle intention prediction","author":"Hu","year":"2022"},{"key":"10.1016\/j.eswa.2026.131585_bib0018","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","article-title":"Inceptiontime: Finding alexnet for time series classification","volume":"34","author":"Ismail Fawaz","year":"2020","journal-title":"Data Mining and Knowledge Discovery"},{"key":"10.1016\/j.eswa.2026.131585_bib0019","series-title":"Machine learning for health","first-page":"126","article-title":"sEMG gesture recognition with a simple model of attention","author":"Josephs","year":"2020"},{"key":"10.1016\/j.eswa.2026.131585_bib0020","series-title":"International joint conference on neural networks","article-title":"Domain adaptation for sEMG-based gesture recognition with recurrent neural networks","author":"Ketyk\u00f3","year":"2019"},{"key":"10.1016\/j.eswa.2026.131585_bib0021","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","article-title":"Selfreg: Self-supervised contrastive regularization for domain generalization","author":"Kim","year":"2021"},{"key":"10.1016\/j.eswa.2026.131585_bib0022","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017","journal-title":"Proceedings of the International Conference on Learning Representations"},{"key":"10.1016\/j.eswa.2026.131585_bib0023","article-title":"Wearable sensors for health monitoring: Current applications, trends, and future directions","volume":"28","author":"Kurul","year":"2026","journal-title":"Biosensors and Bioelectronics: X"},{"key":"10.1016\/j.eswa.2026.131585_bib0024","series-title":"Proceedings of the international conference on neural information processing","article-title":"Stcn-gr: spatial-temporal convolutional networks for surface-electromyography-based gesture recognition","author":"Lai","year":"2021"},{"issue":"1","key":"10.1016\/j.eswa.2026.131585_bib0025","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/s41528-023-00246-3","article-title":"Stretchable array electromyography sensor with graph neural network for static and dynamic gestures recognition system","volume":"7","author":"Lee","year":"2023","journal-title":"npj Flexible Electronics"},{"key":"10.1016\/j.eswa.2026.131585_bib0026","series-title":"Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining","first-page":"1069","article-title":"Graph rationalization with environment-based augmentations","author":"Liu","year":"2022"},{"key":"10.1016\/j.eswa.2026.131585_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127954","article-title":"An end-to-end model for time series classification in the presence of missing values","volume":"284","author":"Liu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131585_bib0028","series-title":"International conference on machine learning, ICML, vienna, austria, july 21-27, 2024","article-title":"CaudiTS: Causal disentangled domain adaptation of multivariate time series","author":"Lu","year":"2024"},{"key":"10.1016\/j.eswa.2026.131585_bib0029","series-title":"Proceedings of the IEEE international conference on acoustics, speech and signal processing","first-page":"3833","article-title":"Local and global alignments for generalizable sensor-based human activity recognition","author":"Lu","year":"2022"},{"key":"10.1016\/j.eswa.2026.131585_bib0030","series-title":"Proceedings of the international conference on machine learning","first-page":"15524","article-title":"Interpretable and generalizable graph learning via stochastic attention mechanism","author":"Miao","year":"2022"},{"issue":"10","key":"10.1016\/j.eswa.2026.131585_bib0031","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0186132","article-title":"Comparison of six electromyography acquisition setups on hand movement classification tasks","volume":"12","author":"Pizzolato","year":"2017","journal-title":"PloS One"},{"key":"10.1016\/j.eswa.2026.131585_bib0032","series-title":"Proceedings of the AAAI conference on artificial intelligence","article-title":"Latent independent excitation for generalizable sensor-based cross-person activity recognition","author":"Qian","year":"2021"},{"key":"10.1016\/j.eswa.2026.131585_bib0033","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"15900","article-title":"Bi-level meta-learning for few-shot domain generalization","author":"Qin","year":"2023"},{"key":"10.1016\/j.eswa.2026.131585_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125755","article-title":"A novel two-enhancive aspect module in convolutional neural networks for multivariate time series classification","volume":"266","author":"Qiu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131585_bib0035","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jprocont.2015.02.006","article-title":"Enhancing dynamic soft sensors based on DPLS: A temporal smoothness regularization approach","volume":"28","author":"Shang","year":"2015","journal-title":"Journal of Process Control"},{"key":"10.1016\/j.eswa.2026.131585_bib0036","series-title":"Proceedings of the ACM conference on embedded networked sensor systems","first-page":"127","article-title":"Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition","author":"Stisen","year":"2015"},{"key":"10.1016\/j.eswa.2026.131585_bib0037","series-title":"Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining","first-page":"1696","article-title":"Causal attention for interpretable and generalizable graph classification","author":"Sui","year":"2022"},{"key":"10.1016\/j.eswa.2026.131585_bib0038","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"2209","article-title":"Contrastive domain adaptation","author":"Thota","year":"2021"},{"key":"10.1016\/j.eswa.2026.131585_bib0039","series-title":"Proceedings of the IEEE information theory workshop","first-page":"1","article-title":"Deep learning and the information bottleneck principle","author":"Tishby","year":"2015"},{"key":"10.1016\/j.eswa.2026.131585_bib0040","series-title":"Ieee international conference on acoustics, speech and signal processing","article-title":"Improved gesture recognition based on sEMG signals and TCN","author":"Tsinganos","year":"2019"},{"key":"10.1016\/j.eswa.2026.131585_bib0041","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"8","key":"10.1016\/j.eswa.2026.131585_bib0042","first-page":"8052","article-title":"Generalizing to unseen domains: A survey on domain generalization","volume":"35","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"8","key":"10.1016\/j.eswa.2026.131585_bib0043","doi-asserted-by":"crossref","first-page":"10253","DOI":"10.1609\/aaai.v37i8.26221","article-title":"Sensor alignment for multivariate time-series unsupervised domain adaptation","volume":"37","author":"Wang","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.131585_bib0044","first-page":"20437","article-title":"Graph information bottleneck","volume":"33","author":"Wu","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131585_bib0045","article-title":"FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective","volume":"36","author":"Yi","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131585_bib0046","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., & Deny, S. (2021). Barlow twins: Self-supervised learning via redundancy reduction. arXiv: 2103.03230."},{"key":"10.1016\/j.eswa.2026.131585_bib0047","series-title":"Proceedings of the conference on health, inference, and learning","first-page":"279","article-title":"An empirical framework for domain generalization in clinical settings","author":"Zhang","year":"2021"},{"key":"10.1016\/j.eswa.2026.131585_bib0048","article-title":"From canonical correlation analysis to self-supervised graph neural networks","author":"Zhang","year":"2021"},{"key":"10.1016\/j.eswa.2026.131585_bib0049","series-title":"Proceedings of the international conference on learning representations","article-title":"Graph-guided network for irregularly sampled multivariate time series","author":"Zhang","year":"2022"},{"key":"10.1016\/j.eswa.2026.131585_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110304","article-title":"Graph neural networks for multivariate time-series forecasting via learning hierarchical spatiotemporal dependencies","volume":"147","author":"Zhou","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426004987?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426004987?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T04:48:17Z","timestamp":1774414097000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426004987"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":50,"alternative-id":["S0957417426004987"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131585","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Harnessing spatial dependency for domain generalization in multivariate time-series sensor data","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131585","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"131585"}}