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Sensor-Based Time-Series Analysis Empowered by Artificial Intelligence
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Sensor-Based Time-Series Analysis Empowered by Artificial Intelligence

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 15 November 2026 | Viewed by 1721

Special Issue Editors

Dr. Weidong Dang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: time series; artificial intelligence; network science; new sensor technology; brain-computer fusion; multiphase flow; intelligent medicine
Prof. Dr. Zhong-Ke Gao

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: multi-source information fusion; new sensor technology; network science; multiphase flow detection; brain-computer fusion and hybrid intelligence; intelligent medicine; brain-controlled rehabilitation robots
Prof. Dr. Chengyi Xia

E-Mail Website
Guest Editor
School of Artificial Intelligence, Tiangong University, Tianjin 300387, China
Interests: complex systems and complex networks; complexity theory in artificial intelligence; evolutionary game theory
Dr. Dongmei Lv

E-Mail Website
Guest Editor Assistant
School of Artificial Intelligence, Tiangong University, Tianjin 300387, China
Interests: motor imagery; EEG decoding; EEG classification; convolutional neural network

Special Issue Information

Dear Colleagues,

Rapid advances in Artificial Intelligence and Network Science are reshaping sensor-based time-series modeling, with broad applicability across domains and settings. Current research spans self-supervised and generative learning, state-space models, efficient long-sequence modeling, dynamic graphs, and topological structure learning. Meanwhile, common challenges in real-world data, such as long-range dependencies, asynchronous/irregular sampling, low SNR with structured artifacts, and covariate or concept drift, raise higher demands on generalization, interpretability, and engineering deployment.

This Special Issue centers on “sensor data-driven time-series methods and systems”, in alignment with the scope of Sensors. It welcomes application-agnostic research aimed at advancing foundational theory, algorithmic methods, and efficient systems for time-series analysis. We especially encourage work at the intersection of Sensor-based Time Series × Artificial Intelligence × Network Science and place no restrictions on application areas, welcoming signal-analysis submissions from diverse domains and settings (for instance, biomedical signals, physiological monitoring and brain-computer interface technology). Any method or practice centered on sensor or sensed data falls within the scope of this Special Issue.

Dr. Weidong Dang
Prof. Dr. Zhong-Ke Gao
Prof. Dr. Chengyi Xia
Guest Editors

Dr. Dongmei Lv
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor data-driven time-series modeling
  • artificial intelligence for sensor signal analysis
  • network science for time-series analysis
  • physiological and biomedical signal analysis
  • brain-computer interfaces and their applications
  • industrial and IoT sensor time series
  • forecasting and control with sensor networks

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Published Papers (2 papers)

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Research

23 pages, 1994 KB  
Article
A Radar-Based Contactless System for Joint Phonocardiogram Reconstruction and Cardiac State Segmentation Using a Self-Attention 1D U-Net
by Giulio Montanari, Marco Mura, Pasquale Di Viesti, Elia Vignoli, Giorgio Guerzoni and Giorgio Matteo Vitetta
Sensors 2026, 26(10), 3151; https://doi.org/10.3390/s26103151 - 15 May 2026
Viewed by 294
Abstract
Contactless vital signs monitoring is becoming increasingly relevant in scenarios where conventional sensors are impractical or not recommended. In this manuscript, a radar-based contactless system for the joint reconstruction of phonocardiogram (PCG) waveforms and cardiac state segmentation is illustrated. The proposed method exploits [...] Read more.
Contactless vital signs monitoring is becoming increasingly relevant in scenarios where conventional sensors are impractical or not recommended. In this manuscript, a radar-based contactless system for the joint reconstruction of phonocardiogram (PCG) waveforms and cardiac state segmentation is illustrated. The proposed method exploits a self-attention one-dimensional (1D) U-Net fed by a pre-processed radar-derived input to estimate a PCG-like waveform, its envelope, and the four main cardiac phases: S1, systole, S2, and diastole. The accuracy of our method has been assessed on a public synchronized radar–PCG dataset acquired by means of a 24 GHz Doppler radar and a digital stethoscope. On the test subset, the proposed model achieved a 13.4885 dB reduction in log-spectral distance relative to the radar input signal, indicating a marked improvement in waveform fidelity. Segmentation performance also improved, with Micro-F1 increasing from 74.41% to 84.17% and Macro-F1 from 68.40% to 80.43% on average. Experimental results demonstrated the viability of real-time low-power embedded hardware deployment for contactless auscultation and continuous cardiac monitoring applications. The findings confirm that respiratory interference and low-amplitude signals complicate S2 detection, especially when exacerbated by subject motion. Full article
(This article belongs to the Special Issue Sensor-Based Time-Series Analysis Empowered by Artificial Intelligence)
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17 pages, 4164 KB  
Article
Multi-Scale Spatiotemporal Graph Neural Network Using Brain Partitioning for Major Depressive Disorder Detection
by Zhao Geng, Wei Guo, Jiale Wang, Yonghua Ma and Yongbao Zhu
Sensors 2026, 26(9), 2868; https://doi.org/10.3390/s26092868 - 4 May 2026
Viewed by 1088
Abstract
Major depressive disorder (MDD) is a prevalent and severe mental disorder, and EEG-based automated detection has become a promising approach for auxiliary screening diagnosis. In this work, we propose a novel multiscale spatiotemporal graph neural network for MDD detection from multichannel EEG signals. [...] Read more.
Major depressive disorder (MDD) is a prevalent and severe mental disorder, and EEG-based automated detection has become a promising approach for auxiliary screening diagnosis. In this work, we propose a novel multiscale spatiotemporal graph neural network for MDD detection from multichannel EEG signals. Specifically, a left–right hemispheric partitioning prior is used to encode brain functional organization. Based on this partitioning, adaptive graphs are then constructed and graph message passing is performed to model intra-hemispheric interactions. The approach not only incorporates brain functional organization into the learning process but also enhances the extraction of discriminative features related to depressive brain dynamics. The proposed method was validated in a cross-subject scenario on a private resting-state EEG dataset including 54 adult participants (27 MDD patients and 27 healthy controls; age range: 27–48 years). Experimental results on the dataset achieve an accuracy of 92.21%, surpassing the baseline models. Meanwhile, ablation experiments demonstrate the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Sensor-Based Time-Series Analysis Empowered by Artificial Intelligence)
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