ReviewSensors (Basel, Switzerland)2022
Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Artificial Intelligence in Electrochemical Sensing: A Network Evidence Map of Translational Barriers and Pathways to Point-of-Care Deployment.Biosensors · 2026Review
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation.Communications biology · 2026Article
- [A time-frequency transform and Riemannian manifold-based domain adaptation method for motor imagery in brain source space].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Hybrid real-synthetic dataset framework for robotic hazard detection in industrial environments.Scientific reports · 2026Article
- Transcriptomic exploration of key genes related to mitochondria and ferroptosis in inflammatory bowel disease and experimental validation.Scientific reports · 2025Article
- Numerical Simulation Data-Aided Domain-Adaptive Generalization Method for Fault Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Unsupervised Domain Adaptation Method Based on Relative Entropy Regularization and Measure Propagation.Entropy (Basel, Switzerland) · 2025Article
- Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation.Sensors (Basel, Switzerland) · 2025Article
- Beyond Information Distortion: Imaging Variable-Length Time Series Data for Classification.Sensors (Basel, Switzerland) · 2025Article
- Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma.BMC cancer · 2025Article
- Reducing Cross-Sensor Domain Gaps in Tactile Sensing via Few-Sample-Driven Style-to-Content Unsupervised Domain Adaptation.Sensors (Basel, Switzerland) · 2025Article
- Identification and Validation of Oxidative Stress-Related Diagnostic Marker Genes and Immune Landscape in Ulcerative Interstitial Cystitis by Integrating Bioinformatics and Machine Learning.Journal of inflammation research · 2025Article
- Identification of novel immune-related signatures for keloid diagnosis and treatment: insights from integrated bulk RNA-seq and scRNA-seq analysis.Human genomics · 2024Article
- CALDA: Improving Multi-Source Time Series Domain Adaptation With Contrastive Adversarial Learning.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios,
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.