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Flexible Wearable Sensors for Biomechanical Applications
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Flexible Wearable Sensors for Biomechanical Applications

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 7545

Editor

Dr. Manuel E. Hernandez

E-Mail Website
Guest Editor
Neuroscience Program, University of Illinois Urbana-Champaign (UIUC), Urbana, IL 61801, USA
Interests: mobility; balance; gait; aging; neurological disorders; neuroimaging; biomechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in material science, bioinstrumentation, and signal processing and analysis have provided novel sensing technologies that are compliant, discreet, and more comfortable to adopt for use in biomechanical applications such as injury prevention, recovery, performance enhancement, etc. Flexible wearable sensors combined with advanced machine learning or artificial intelligence allow for advancements in the diagnosis, monitoring, and prediction of changes in physical function and health.

This Special Issue, “Flexible Wearable Sensors for Biomechanical Applications”, will aim to showcase original contributions that explore the use of flexible wearable sensors for biomechanical applications, covering a wide range of topics, including but not limited to the following:

  • Novel devices or flexible wearable sensors for biomechanical applications;
  • Use of flexible wearable sensors in biomechanical applications, such as injury prevention, recovery, or performance enhancement, among others;
  • Innovative sensor design, programming, or integration to enhance the performance, usability, and wearability of flexible sensors;
  • Integration of flexible wearable sensors for multi-domain approaches to biomechanical analyses;
  • Novel algorithms and methods for processing, analyzing, and interpreting flexible wearable sensor data.

Dr. Manuel E. Hernandez
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-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

  • multimodal sensors
  • flexible wearable sensors
  • biomechanics
  • performance assessment
  • rehabilitation
  • injury prevention
  • machine learning
  • artificial intelligence

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Published Papers (4 papers)

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Research

Jump to: Review

9 pages, 687 KB  
Communication
Evaluating the Psychometrics of Accelerometer Data for Independent Monitoring of Task Repetitive Practice
by Elena V. Donoso Brown, Rachael Miller Neilan, Fiona Kessler Brody, Jenna Gallipoli, Taylor McElroy and MacKenzie Gough
Sensors 2025, 25(21), 6686; https://doi.org/10.3390/s25216686 - 1 Nov 2025
Viewed by 870
Abstract
Individuals post-stroke commonly experience impairments in upper extremity function that limit participation in valued activities. Task repetitive practice is an effective intervention strategy, but accurately monitoring adherence and movement quality in home programs remains a challenge. This pilot study investigates the reliability and [...] Read more.
Individuals post-stroke commonly experience impairments in upper extremity function that limit participation in valued activities. Task repetitive practice is an effective intervention strategy, but accurately monitoring adherence and movement quality in home programs remains a challenge. This pilot study investigates the reliability and validity of raw accelerometer data captured by a commercially available, wrist-worn activity monitor to assess upper extremity movement in healthy adults during task repetitive practice. Measures of duration, angular velocity, and acceleration were obtained from activity monitors worn by 25 healthy adults performing four functional tasks under varying conditions. Preliminary results indicate moderate to excellent within-session reliability in these three measures when compared across repeated trials of the same task, with one exception. Across all tasks, the duration measure consistently detected differences in exercise time between sets of 5, 10, and 20 repetitions at a comfortable pace. All three measures differentiated between 10 comfortable repetitions and 10 fast repetitions on three out of four tasks. These findings provide initial psychometric properties in a healthy population and further research is required to determine whether these properties remain robust in the presence of motor impairment. This work represents an early step towards developing approaches for monitoring home exercise programs that support stroke recovery. Full article
(This article belongs to the Special Issue Flexible Wearable Sensors for Biomechanical Applications)
21 pages, 9112 KB  
Article
An Adaptive Grasping Multi-Degree-of-Freedom Prosthetic Hand with a Rigid–Flexible Coupling Structure
by Longhan Wu and Qingcong Wu
Sensors 2025, 25(19), 6034; https://doi.org/10.3390/s25196034 - 1 Oct 2025
Cited by 1 | Viewed by 1842
Abstract
This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which [...] Read more.
This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which can enhance the dexterity of traditional rigid actuators while achieving a human-like workspace. Each finger is designed with a specific degree of rotational freedom to mimic natural opening and closing motions. This study also elaborates on the mapping of eight-channel electromyography to finger grasping force through improved TCN, as well as the control algorithm for grasping flexible objects. A functional prototype of the prosthetic hand was fabricated, and a series of experiments involving adaptive grasping and handheld manipulation tasks were conducted to validate the effectiveness of the proposed mechanical structure and control strategy. The results demonstrate that the hand can stably grasp flexible objects of various shapes and sizes. This work provides a practical solution for prosthetic hand design, offering promising potential for developing lightweight, dexterous, and highly anthropomorphic robotic hands suitable for real-world applications. Full article
(This article belongs to the Special Issue Flexible Wearable Sensors for Biomechanical Applications)
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21 pages, 10466 KB  
Article
Feasibility Study of Using Alternating Current Excitation to Obtain Electrodermal Activity with a Wearable System
by Juan David Romero-Ante, Juan Sebastián Montenegro-Bravo, José María Vicente-Samper, Vicente Manuel Esteve-Sala, Miguel Ángel de la Casa-Lillo and José María Sabater-Navarro
Sensors 2025, 25(12), 3603; https://doi.org/10.3390/s25123603 - 8 Jun 2025
Cited by 1 | Viewed by 3006
Abstract
This study investigates the feasibility of using a wearable system with full-wave alternating current (AC) excitation to measure electrodermal activity (EDA). Typically measured using direct current (DC) excitation, EDA is often affected by signal drift due to electrode–skin polarisation. To address this, a [...] Read more.
This study investigates the feasibility of using a wearable system with full-wave alternating current (AC) excitation to measure electrodermal activity (EDA). Typically measured using direct current (DC) excitation, EDA is often affected by signal drift due to electrode–skin polarisation. To address this, a portable device was developed that applies fixed-amplitude, full-wave AC signals and records EDA under controlled conditions. The electrical behaviour of the skin was also simulated using a multilayer model to analyse current propagation at different frequencies. The experimental procedure was conducted with ten healthy participants under controlled conditions. Two stages were carried out: the first compared the similarity of the skin conductance level (SCL) between DC and half-wave alternating current (AC) signals; the second analysed signal stability and skin response at full-wave AC excitation. Compared to DC, full-wave AC excitation demonstrated reduced signal drift, greater temporal stability, and enhanced measurement of the skin’s capacitive response. These findings support the adoption of AC excitation for EDA measurement, especially in ambulatory and real-time biomechanical applications where signal reliability and stability are essential. Full article
(This article belongs to the Special Issue Flexible Wearable Sensors for Biomechanical Applications)
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Review

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25 pages, 1648 KB  
Review
Freezing of Gait in Parkinson’s Disease: A Scoping Review on the Path Towards Real-Time Therapies
by Meenakshi Singhal, Christina Grannie, Margaret Burnette, Manuel E. Hernandez and Samar A. Hegazy
Sensors 2026, 26(7), 2042; https://doi.org/10.3390/s26072042 - 25 Mar 2026
Viewed by 1170
Abstract
Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of [...] Read more.
Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management. Full article
(This article belongs to the Special Issue Flexible Wearable Sensors for Biomechanical Applications)
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