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Flexible Sensing in Robotics, Healthcare, and Beyond
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Flexible Sensing in Robotics, Healthcare, and Beyond

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 10976

Special Issue Editor

Dr. Yuanyuan Yang

E-Mail Website
Guest Editor
School of Aerospace Engineering, Xiamen University, Xiamen, China
Interests: flexible sensors; soft robotics

Special Issue Information

Dear Colleagues,

Soft robotic systems, combining the flexibility and adaptability of soft materials with the versatility of robots, are revolutionizing various fields, including biomedical engineering, manufacturing, and exploration. In recent years, significant advancements in sensor technologies have propelled the development and application of soft robotic systems, offering enhanced capabilities in sensing, interaction, and adaptability.

This Special Issue focuses on the latest innovations, applications, and challenges in advanced sensors technologies for soft robotic systems. The integration of cutting-edge sensors with soft robots offers the realization of precise control, environmental interaction, and intelligent decision-making in dynamic and unstructured environments.

We welcome the submission of manuscripts that address a range of topics including, but not limited to, the following:

  • Soft sensors and actuators;
  • Tactile and haptic sensing;
  • Flexible and stretchable electronics;
  • Bio-inspired sensor systems;
  • Environmental sensing;
  • Sensor integration and fusion;
  • Real-time data processing and feedback;
  • Applications in biomedical engineering, industry, and beyond.

Dr. Yuanyuan Yang
Guest Editor

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

  • soft sensors
  • soft robotics
  • sensing feedback
  • tactile sensing
  • haptic sensing
  • environmental sensing
  • sensor integration
  • robotic application
  • bio-inspired sensor

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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36 pages, 7729 KB  
Article
FEM-Based Estimation–Correction with Minimal Indentation Set for Internal Cavity Classification and Geometry Estimation in Deformable Objects
by Thibaut Morant, María Cordero-Alvarado, Tianyi Yang, Koshi Kurosawa, Yuto Tanizaki, Nahoko Nagano and Wenwei Yu
Sensors 2026, 26(10), 3022; https://doi.org/10.3390/s26103022 - 11 May 2026
Viewed by 614
Abstract
Accurately estimating the internal structure of deformable objects from sparse measurements remains a significant challenge in robotics. This work proposes a three-stage identification framework for this problem. First, a classification strategy determines a minimal informative set of indentation locations using a generalized error [...] Read more.
Accurately estimating the internal structure of deformable objects from sparse measurements remains a significant challenge in robotics. This work proposes a three-stage identification framework for this problem. First, a classification strategy determines a minimal informative set of indentation locations using a generalized error computed from pre-simulated FEM force reactions of baseline cavity models and flat-punch indentation estimation. Using this set, the estimation stage detects the cavity type and provides a preliminary estimate of its geometric parameters based solely on measured indentation responses. The correction stage then refines these parameters by replaying measured indentation depths in FEM simulations and deriving geometry corrections from the discrepancy between simulated and homogeneous force responses. Robust loss functions at both stages limit the influence of measurements where local contact conditions deviate from the assumed model, improving reliability across all tested cases. Indentation depth was obtained through gripper proprioception, with an RGB-D camera limited to global pose alignment. Experiments on soft cubes with spherical, cuboid, and pyramidal cavities demonstrate that, within known cavity families and fixed material parameters, the minimal indentation set reliably distinguishes cavity types and the pipeline reconstructs dimensions within error bounds. Extending the framework to non-centered structures and unknown materials remains future work. Full article
(This article belongs to the Special Issue Flexible Sensing in Robotics, Healthcare, and Beyond)
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8 pages, 4250 KB  
Communication
A Dual-Mode Flexible Sensor with Capacitive–Resistive Hybrid Response for Bolt Loosening Monitoring
by Yan Ping, Kechen Li, Chao Yuan, Ding Guo and Yuanyuan Yang
Sensors 2026, 26(2), 578; https://doi.org/10.3390/s26020578 - 15 Jan 2026
Viewed by 478
Abstract
The structural health monitoring of bolted connections is important for ensuring the safety and reliability of engineering systems, yet conventional sensing technologies struggle to balance detection range and sensitivity. This study presents a flexible sensor with a hybrid capacitive–resistive sensing mechanism, designed to [...] Read more.
The structural health monitoring of bolted connections is important for ensuring the safety and reliability of engineering systems, yet conventional sensing technologies struggle to balance detection range and sensitivity. This study presents a flexible sensor with a hybrid capacitive–resistive sensing mechanism, designed to overcome the limitations of single-mode sensors. By integrating a hierarchically structured composite layer with tailored material properties, the sensor achieves a seamless transition between sensing modes across different pressure ranges. It exhibits high sensitivity in both low-pressure and high-pressure regions, enabling the reliable detection of preload variations in bolted connections. Experimental validation confirms its cyclic durability and rapid response to mechanical changes, demonstrating good potential for real-time monitoring in aerospace and industrial systems. Full article
(This article belongs to the Special Issue Flexible Sensing in Robotics, Healthcare, and Beyond)
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11 pages, 4787 KB  
Article
Vision-Based Hand Function Evaluation with Soft Robotic Rehabilitation Glove
by Mukun Tong, Michael Cheung, Yixing Lei, Mauricio Villarroel and Liang He
Sensors 2026, 26(1), 138; https://doi.org/10.3390/s26010138 - 25 Dec 2025
Viewed by 975
Abstract
Advances in robotic technology for hand rehabilitation, particularly soft robotic gloves, have significant potential to improve patient outcomes. While vision-based algorithms pave the way for fast and convenient hand pose estimation, most current models struggle to accurately track hand movements when soft robotic [...] Read more.
Advances in robotic technology for hand rehabilitation, particularly soft robotic gloves, have significant potential to improve patient outcomes. While vision-based algorithms pave the way for fast and convenient hand pose estimation, most current models struggle to accurately track hand movements when soft robotic gloves are used, primarily due to severe occlusion. This limitation reduces the applicability of soft robotic gloves in digital and remote rehabilitation assessment. Furthermore, traditional clinical assessments like the Fugl-Meyer Assessment (FMA) rely on manual measurements and subjective scoring scales, lacking the efficiency and quantitative accuracy needed to monitor hand function recovery in data-driven personalised rehabilitation. Consequently, few integrated evaluation systems provide reliable quantitative assessments. In this work, we propose an RGB-based evaluation system for soft robotic glove applications, which is aimed at bridging these gaps in assessing hand function. By incorporating the Hand Mesh Reconstruction (HaMeR) model fine-tuned with motion capture data, our hand estimation framework overcomes occlusion and enables accurate continuous tracking of hand movements with reduced errors. The resulting functional metrics include conventional clinical benchmarks such as the mean per joint angle error (MPJAE) and range of motion (ROM), providing quantitative, consistent measures of rehabilitation progress and achieving tracking errors lower than 10°. In addition, we introduce adapted benchmarks such as the angle percentage of correct keypoints (APCK), mean per joint angular velocity error (MPJAVE) and angular spectral arc length (SPARC) error to characterise movement stability and smoothness. This extensible and adaptable solution demonstrates the potential of vision-based systems for future clinical and home-based rehabilitation assessment. Full article
(This article belongs to the Special Issue Flexible Sensing in Robotics, Healthcare, and Beyond)
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9 pages, 2022 KB  
Communication
Human Skin-Inspired Staggered Microstructures for Optimizing Sensitivity of Flexible Pressure Sensor
by Kechen Li and Yuanyuan Yang
Sensors 2025, 25(8), 2415; https://doi.org/10.3390/s25082415 - 11 Apr 2025
Cited by 3 | Viewed by 2025
Abstract
Flexible pressure sensors play a significant role in wearable electronics, human–machine interfaces, and health monitoring, and improving their performance has always been a major focus of research. Various microstructures have been proposed to enhance sensitivity, particularly when tilted. However, unidirectional tilting may create [...] Read more.
Flexible pressure sensors play a significant role in wearable electronics, human–machine interfaces, and health monitoring, and improving their performance has always been a major focus of research. Various microstructures have been proposed to enhance sensitivity, particularly when tilted. However, unidirectional tilting may create a shift in contact surfaces, reducing accuracy in pressure detection. To address these limitations, this study introduces a capacitive pressure sensor with a staggered tilted column microstructure, inspired by the elaborate network of epidermis and dermis layers within human skin. The simulation and experiment results reveal that the developed sensor has high sensitivity and responds rapidly to applied forces, making it suitable for real-time applications. Demonstrations of gesture recognition and physiological monitoring highlight its practical potential. These findings underscore the effectiveness of the staggered microstructure in improving sensor performance and its applicability in next-generation flexible sensors. Full article
(This article belongs to the Special Issue Flexible Sensing in Robotics, Healthcare, and Beyond)
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Review

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29 pages, 2112 KB  
Review
From Sensors to Care: How Robotic Skin Is Transforming Modern Healthcare—A Mini Review
by Yuting Zhu, Wendy Moyle, Min Hong and Kean Aw
Sensors 2025, 25(9), 2895; https://doi.org/10.3390/s25092895 - 3 May 2025
Cited by 11 | Viewed by 6076
Abstract
In recent years, robotics has made notable progress, becoming an essential component of daily life by facilitating complex tasks and enhancing human experiences. While most robots have traditionally featured hard surfaces, the growing demand for more comfortable and safer human–robot interactions has driven [...] Read more.
In recent years, robotics has made notable progress, becoming an essential component of daily life by facilitating complex tasks and enhancing human experiences. While most robots have traditionally featured hard surfaces, the growing demand for more comfortable and safer human–robot interactions has driven the development of soft robots. One type of soft robot, which incorporates innovative skin materials, transforms rigid structures into more pliable and adaptive forms, making them better suited for interacting with humans. Especially in healthcare and rehabilitation, robotic skin technology has gained substantial attention, offering transformative solutions for improving the functionality of prosthetics, exoskeletons, and companion robots. Although replicating the complex sensory functions of human skin remains a challenge, ongoing research in soft robotics focuses on developing sensors that mimic the softness and tactile sensitivity necessary for effective interaction. This review provides a narrative analysis of current trends in robotic skin development, specifically tailored for healthcare and rehabilitation applications, including skin types of sensor technologies, materials, challenges, and future research directions in this rapidly developing field. Full article
(This article belongs to the Special Issue Flexible Sensing in Robotics, Healthcare, and Beyond)
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