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Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems
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Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1896

Special Issue Editors

Dr. Chen Yin

E-Mail Website
Guest Editor
Department of Data Science, College of Computing, City University of Hong Kong, Hong Kong 999077, China
Interests: signal processing; data analytics; fault diagnosis; health prognostic; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Bingchang Hou

E-Mail Website
Guest Editor
State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China
Interests: machinery condition monitoring, fault diagnosis, and prognostics; intelligent autonomous systems; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Lai Hu

E-Mail Website
Guest Editor
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, China
Interests: high-grade CNC machine tools; high performance machining; milling; grinding; bearing
Special Issues, Collections and Topics in MDPI journals
Dr. Qun Wang

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: energy management; eco-driving; deep reinforcement learning; sensor signal processing for intelligent transportation systems; vehicle sensor data analytics

Special Issue Information

Dear Colleagues,

With the rapid development of advanced sensing technologies, intelligent data analytics and sensor signal analysis has become a cornerstone for intelligent health management and autonomous systems across a wide range of engineering domains. Modern industrial equipment, robotic systems, and autonomous vehicles rely heavily on multi-modal sensor data to perceive their operating conditions, assess system health, and make autonomous decisions in complex and uncertain environments.

This Special Issue focuses on recent advances in sensor signal analysis methods for intelligent health management and autonomous systems. Intelligent health management includes sensor-based fault diagnosis, degradation assessment, and remaining useful life prediction, aiming to improve system reliability, safety, and operational efficiency. Autonomous systems require robust sensing, perception, and real-time data interpretation to achieve adaptive, safe, and efficient autonomous operation.

Contributions are invited on novel sensor signal processing techniques, data-driven and physics-informed learning methods, and multi-sensor fusion strategies that support both health management and autonomy. Particular emphasis is placed on applications such as intelligent manufacturing, sensor-based robotic machining and assembling, surface quality monitoring, and sensing and perception for autonomous vehicles. This Special Issue aims to bring together interdisciplinary research that advances sensor signal analysis methodologies and their applications in intelligent health management and autonomous systems.

Dr. Chen Yin
Dr. Bingchang Hou
Dr. Lai Hu
Dr. Qun Wang
Guest Editors

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-based fault diagnosis and remaining useful life prediction
  • condition monitoring and degradation assessment using sensor data
  • advanced signal processing and feature learning for multi-modal sensors
  • physics-informed and hybrid learning approaches for health management
  • multi-sensor data fusion for system health assessment and autonomy
  • sensing and perception for robotic machining and assembling
  • sensor-based surface quality monitoring and defect detection
  • sensor signal analysis for autonomous vehicles and mobile robots
  • explainable and trustworthy ai for sensor-based health management
  • digital twin technologies driven by sensor signals
  • robust sensing and diagnostics under uncertainty and harsh environments

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Published Papers (2 papers)

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Research

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38 pages, 4934 KB  
Article
Automated Ergonomic Risk Assessment of Wheelchair Users During Cabinet Interaction Using Vision-Based 3D Pose Estimation
by Yilin Xu, Ziqian Yang, Tao Sun and Jiachuan Ning
Sensors 2026, 26(9), 2893; https://doi.org/10.3390/s26092893 - 5 May 2026
Viewed by 955
Abstract
Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated [...] Read more.
Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated ergonomic risk assessment of wheelchair users during cabinet interaction. The proposed framework integrates YOLOv11 for human detection, MHFormer for monocular 3D pose reconstruction, and a fuzzy logic-enhanced RULA model for continuous ergonomic risk quantification from video-derived motion signals. To support model development and evaluation, we constructed a dedicated wheelchair cabinet-operation dataset comprising 30 participants, including 14 experienced wheelchair users and 16 trained simulation participants, across five representative cabinet-operation scenarios. The raw dataset contained approximately 5 h of RGB video and about 150,000 original frames. To reduce redundancy caused by highly similar consecutive frames and to mitigate overfitting risk, representative frames were sampled from the continuous video sequences, resulting in 10,000 images for annotation and model development. Based on the proposed framework, raw visual sensor signals are transformed into temporally continuous kinematic representations and ergonomic risk scores, enabling non-contact and real-time health-state interpretation in assistive living environments. The proposed method achieved an average joint-angle estimation RMSE of 7.5°, representing an approximately 60% reduction compared with a Kinect v2-based motion capture baseline (18.6°), which is widely used for low-cost ergonomic evaluation. In benchmark evaluation, the proposed method achieved 84% risk-classification accuracy with a Cohen’s kappa of 0.66, outperforming representative baseline approaches. The results further indicated that low revolving-door and low-drawer operations were associated with higher and more sustained ergonomic risk exposure than sliding-door interaction. These findings demonstrate that vision-based sensor signal analysis can provide an effective solution for intelligent health management, ergonomic monitoring, and perception-driven assessment in accessible and assistive autonomous living systems. Full article
(This article belongs to the Special Issue Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems)
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Review

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34 pages, 2094 KB  
Review
Sensor-Driven Deep Learning for Smart Home Intelligence: Signal Analysis, Multimodal Perception, and System-Level Applications
by Chenchen Wu, Ziqian Yang and Tao Sun
Sensors 2026, 26(10), 2993; https://doi.org/10.3390/s26102993 - 9 May 2026
Viewed by 584
Abstract
Smart home environments are evolving toward context-aware intelligent systems with the rapid integration of the Internet of Things (IoT), edge computing, and artificial intelligence. In such settings, large volumes of heterogeneous sensor data must be continuously processed to support perception, behavior understanding, and [...] Read more.
Smart home environments are evolving toward context-aware intelligent systems with the rapid integration of the Internet of Things (IoT), edge computing, and artificial intelligence. In such settings, large volumes of heterogeneous sensor data must be continuously processed to support perception, behavior understanding, and autonomous decision-making. Deep learning has emerged as a key approach for transforming raw sensor signals into structured representations that enable these functions. This review examines recent advances in deep learning for smart home applications from a sensor-driven perspective. Existing studies are organized into five major domains: human activity recognition, health monitoring and assisted living, smart energy management, security monitoring and anomaly detection, and voice interaction and intelligent control. Representative methodological paradigms—including convolutional and recurrent neural networks, Transformers, graph-based learning, multimodal fusion, and deep reinforcement learning—are discussed with emphasis on their roles in signal representation, multimodal integration, and decision-oriented modeling. Despite notable progress, several challenges continue to limit real-world deployment. These include the scarcity of high-quality labeled data, privacy and security concerns associated with continuous sensing, limited generalization across environments and users, constraints of edge devices, and the limited interpretability of model output. Addressing these issues requires advances not only in model design but also in data-efficient learning, privacy-preserving architectures, and system-level integration. Future research is expected to focus on multimodal perception, distributed and edge intelligence, knowledge-enhanced modeling, and human-centered explainable systems. By synthesizing current developments and highlighting open challenges, this review aims to support the development of robust and deployable deep learning solutions for next-generation smart home systems. Full article
(This article belongs to the Special Issue Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems)
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