Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
4.0
Journals
Sensors
Special Issues
Soft Bioelectronic Sensors and Robotic Interfaces for Human-Centered Applications
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Soft Bioelectronic Sensors and Robotic Interfaces for Human-Centered Applications

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 1583

Editors

Dr. Hodam Kim

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
Interests: soft bioelectronics; human–machine interfaces; advanced signal processing
Dr. Woon-Hong Yeo

E-Mail Website
Guest Editor
Woodruff School of Mechanical Engineering, Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, GA, USA
Interests: sensors; bioelectronics; human-machine interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in soft materials and flexible electronics are promoting the development of biointegrated sensors and robotic systems that can interface closely with the human body. This Special Issue focuses on the design of soft bioelectronic sensors and robotic interfaces that provide natural and seamless interaction with human users, supporting applications such as physiological signal monitoring, adaptive actuation, and intelligent feedback.

We welcome original research on soft and stretchable sensors that capture diverse biosignals (e.g., EEG, EMG, PPG, SCG), wearable systems for continuous health monitoring, and conformable devices for robust human–machine interaction. Studies that address the application of soft actuators, biohybrid robotics, and closed-loop control strategies in rehabilitation, assistive technologies, and neuroadaptive systems are also of interest.

We particularly welcome contributions that integrate signal processing, machine learning, and system-level design for applications in digital health, AR/VR environments, and brain–computer interfaces (BCIs).

By compiling interdisciplinary research spanning materials science, biomedical engineering, and robotics, this Special Issue aims to showcase recent progress and future directions in human-centered soft robotic technologies.

Dr. Hodam Kim
Dr. Woon-Hong Yeo
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-anonymized 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 bioelectronic sensors
  • wearable systems
  • human–machine interfaces
  • soft robotics
  • physiological signal monitoring
  • flexible/stretchable electronics
  • neuroadaptive systems
  • brain-computer interfaces (BCI)
  • soft actuators
  • digital health technologies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

28 pages, 33265 KB  
Article
Real-Time Kinematic Reconstruction of Human Lower Limbs Using a 3-IMU Wearable Sensor Network, Transformer Model, and Deployable Edge Computing
by Yang Yu, Wei Dong, Hui Dong, Wenda Wang, Yongzhuo Gao, Dongmei Wu and Weiqi Lin
Sensors 2026, 26(12), 3706; https://doi.org/10.3390/s26123706 - 10 Jun 2026
Viewed by 352
Abstract
Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system [...] Read more.
Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios. Full article
(This article belongs to the Special Issue Soft Bioelectronic Sensors and Robotic Interfaces for Human-Centered Applications)
►▼ Show Figures

Figure 1

16 pages, 1304 KB  
Article
Wearable Functional Near-Infrared Spectroscopy (fNIRS) Monitoring of Prefrontal Activation and Connectivity During Purpose-Driven Consumption
by Daeun Kim, SuJin Bak, Sungkean Kim and Jaeyoung Shin
Sensors 2026, 26(10), 3097; https://doi.org/10.3390/s26103097 - 14 May 2026
Viewed by 567
Abstract
This study investigated the cortical activation patterns and functional connectivity underlying human decision-making by comparing two distinct purchasing orientations: other-oriented consumption (OOC) and self-oriented consumption (SOC), using functional near-infrared spectroscopy (fNIRS) as a wearable neuroimaging modality. The results revealed significant temporal concentration differences [...] Read more.
This study investigated the cortical activation patterns and functional connectivity underlying human decision-making by comparing two distinct purchasing orientations: other-oriented consumption (OOC) and self-oriented consumption (SOC), using functional near-infrared spectroscopy (fNIRS) as a wearable neuroimaging modality. The results revealed significant temporal concentration differences in ∆HbO under the OOC condition in Ch06 (p < 0.05). The 15 fNIRS channels were mapped to seven anatomically defined regions of interest (ROIs) to better capture regional activation patterns and functional network properties. While global network metrics showed no significant differences, seed-based connectivity analysis revealed that the OOC condition elicited significantly stronger functional connectivity between the medial prefrontal cortex (ROI4) and the left lower PFC (ROI6, p < 0.05, d = 0.45). In summary, while the overall network efficiency remained stable across conditions, our findings highlight a spatially specific enhancement in functional connectivity centered on the PFC, indicating an increased cognitive load from engaging in complex social cognitive processes. These findings advance the understanding of neural correlates underlying human decision-making and demonstrate the utility of wearable monitoring using fNIRS for capturing cognitive state differences in human-centered decision contexts. Full article
(This article belongs to the Special Issue Soft Bioelectronic Sensors and Robotic Interfaces for Human-Centered Applications)
►▼ Show Figures

Figure 1

Other

Jump to: Research

42 pages, 15592 KB  
Perspective
Rethinking Brain–Computer Interfaces for Soft Robotic Systems: A Unified Framework and Perspective
by Yizheng Liu, Qian Hu, Xing Wang, Damith Herath and Min Wang
Sensors 2026, 26(12), 3726; https://doi.org/10.3390/s26123726 - 11 Jun 2026
Viewed by 210
Abstract
Soft robotics enables inherently safe, compliant interaction, yet integrating brain–computer interfaces (BCIs) remains hindered by a fundamental mismatch: BCIs typically output low-bandwidth, discrete commands, whereas soft robots possess high-dimensional, nonlinear dynamics. In this position paper, we argue that BCI–soft robot integration must move [...] Read more.
Soft robotics enables inherently safe, compliant interaction, yet integrating brain–computer interfaces (BCIs) remains hindered by a fundamental mismatch: BCIs typically output low-bandwidth, discrete commands, whereas soft robots possess high-dimensional, nonlinear dynamics. In this position paper, we argue that BCI–soft robot integration must move beyond direct decoder-to-actuator mapping. We propose a unified, application-oriented compatibility framework that structurally decouples hierarchical control and formally allocates authority between human neural input and local soft robotic autonomy. Crucially, we introduce verifiable, quantitative design principles that define integration as a matching problem across neural bandwidth, update frequency, latency tolerance, and control dimensionality. Through these testable hypotheses, we demonstrate that active, reactive, and passive BCIs serve distinct, complementary roles. We conclude that shared-control strategies—where the BCI provides high-level intent, target selection, or user-state feedback, while the soft robot manages low-level physical execution and interaction—offer the most practical pathway forward. We argue that future progress depends on the co-design of paradigm, decoding, control, and embodiment for neuro-adaptive and human-centred soft robotic systems. Full article
(This article belongs to the Special Issue Soft Bioelectronic Sensors and Robotic Interfaces for Human-Centered Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop