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Wearable Robotics and Assistive Devices
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Wearable Robotics and Assistive Devices

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 4798

Special Issue Editors

Dr. Jesús Ortiz

E-Mail Website
Guest Editor
Advanced Robotics, Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genova, Italy
Interests: exoskeletons; wearable robotics; exosuits; rehabilitation; mechatronics; human–robot interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Lazzaroni

E-Mail Website
Guest Editor
Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genova, Italy
Interests: exoskeletons; wearable technologies

Special Issue Information

Dear Colleagues,

Wearable assistive robots are potential solutions for the needs of diverse population groups, including persons with disabilities and workers who perform strenuous physical tasks. Persons with weakened limbs may use exoskeletons to augment their strength or to train lost motor abilities. Workers can employ assistive wearable technologies to avoid injuries and enhance performance while executing repetitive and demanding manual material handling tasks. In particular, in Industry 4.0, smart factories are currently using an increasing number of different robotics solutions. Wearable devices are available to monitor workers’ posture or detect excessive and risky compression forces. Alternatively, exoskeletons have been developed to assist workers in performing their tasks by supporting the different human joints to reduce physical loading.

This Special Issue will discuss novel approaches, challenges, and potential solutions in the field of wearable robotics and assistive devices. The aim is to facilitate innovation and bring these technologies closer to wide real-world adoption by collecting and discussing the latest research advances that offer new solutions for developing robust assistive robots and evaluating their effectiveness in real-world scenarios.

Dr. Jesús Ortiz
Dr. Maria Lazzaroni
Guest Editors

Manuscript Submission Information

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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

  • wearable robotics
  • assistive devices
  • exoskeletons
  • physical workload
  • industry
  • rehabilitation
  • human–robot interaction
  • human performance augmentation
  • ergonomics

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Published Papers (4 papers)

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Research

27 pages, 22376 KiB  
Article
Performance Evaluation of Monocular Markerless Pose Estimation Systems for Industrial Exoskeletons
by Soocheol Yoon, Ya-Shian Li-Baboud, Ann Virts, Roger Bostelman, Mili Shah and Nishat Ahmed
Sensors 2025, 25(9), 2877; https://doi.org/10.3390/s25092877 - 2 May 2025
Viewed by 304
Abstract
Industrial exoskeletons (a.k.a. wearable robots) have been developed to reduce musculoskeletal fatigue and work injuries. Human joint kinematics and human–robot alignment are important measurements in understanding the effects of industrial exoskeletons. Recently, markerless pose estimation systems based on monocular color (red, green, blue—RGB) [...] Read more.
Industrial exoskeletons (a.k.a. wearable robots) have been developed to reduce musculoskeletal fatigue and work injuries. Human joint kinematics and human–robot alignment are important measurements in understanding the effects of industrial exoskeletons. Recently, markerless pose estimation systems based on monocular color (red, green, blue—RGB) and depth cameras are being used to estimate human joint positions. This study analyzes the performance of monocular markerless pose estimation systems on human skeletal joint estimation while wearing exoskeletons. Two pose estimation systems producing RGB and depth images from ten viewpoints are evaluated for one subject in 14 industrial poses. The experiment was repeated for three different types of exoskeletons on the same subject. An optical tracking system (OTS) was used as a reference system. The image acceptance rate was 56% for the RGB, 22% for the depth, and 78% for the OTS pose estimation system. The key sources of pose estimation error were the occlusions from the exoskeletons, industrial poses, and viewpoints. The reference system showed decreased performance when the optical markers were occluded by the exoskeleton or when the markers’ position shifted with the exoskeleton. This study performs a systematic comparison of two types of monocular markerless pose estimation systems and an optical tracking system, as well as a proposed metric, based on a tracking quality ratio, to assess whether a skeletal joint estimation would be acceptable for human kinematics analysis in exoskeleton studies. Full article
(This article belongs to the Special Issue Wearable Robotics and Assistive Devices)
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22 pages, 12622 KiB  
Article
Development and Validation of a Modular Sensor-Based System for Gait Analysis and Control in Lower-Limb Exoskeletons
by Giorgos Marinou, Ibrahima Kourouma and Katja Mombaur
Sensors 2025, 25(8), 2379; https://doi.org/10.3390/s25082379 - 9 Apr 2025
Viewed by 684
Abstract
With rapid advancements in lower-limb exoskeleton hardware, two key challenges persist: the accurate assessment of user biomechanics and the reliable control of device behavior in real-world settings. This study presents a modular, sensor-based system designed to enhance both biomechanical evaluation and control of [...] Read more.
With rapid advancements in lower-limb exoskeleton hardware, two key challenges persist: the accurate assessment of user biomechanics and the reliable control of device behavior in real-world settings. This study presents a modular, sensor-based system designed to enhance both biomechanical evaluation and control of lower-limb exoskeletons, leveraging advanced sensor technologies and fuzzy logic. The system addresses the limitations of traditional lab-bound, high-cost methods by integrating inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles. These components work independently or in unison to capture critical biomechanical metrics, including the anteroposterior center of pressure and crutch ground reaction forces. Data are processed in real time by a central unit using fuzzy logic algorithms to estimate gait phases and support exoskeleton control. Validation experiments with three participants, benchmarked against motion capture and force plate systems, demonstrate the system’s ability to reliably detect gait phases and accurately measure biomechanical parameters. By offering an open-source, cost-effective design, this work contributes to the advancement of wearable robotics and promotes broader innovation and accessibility in exoskeleton research. Full article
(This article belongs to the Special Issue Wearable Robotics and Assistive Devices)
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20 pages, 3715 KiB  
Article
L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment
by Manuel Pérez-Soto, Javier Marín and José J. Marín
Sensors 2025, 25(5), 1340; https://doi.org/10.3390/s25051340 - 22 Feb 2025
Viewed by 2044
Abstract
Companies increasingly implement exoskeletons in their production lines to reduce musculoskeletal disorders. Studies have been conducted on the general ergonomic effects of exoskeletons in production environments; however, it remains challenging to predict the biomechanical effects these devices may have in specific jobs. This [...] Read more.
Companies increasingly implement exoskeletons in their production lines to reduce musculoskeletal disorders. Studies have been conducted on the general ergonomic effects of exoskeletons in production environments; however, it remains challenging to predict the biomechanical effects these devices may have in specific jobs. This article proposes the parametric modeling of an active lumbar exoskeleton using the Forces ergonomic method, which calculates the ergonomic risk using motion capture in the workplace, considering the internal joint forces. The exoskeleton was studied to model it in the Forces method using a four-phase approach based on experimental observations (Phase 1) and objective data collection via motion capture with inertial sensors and load cells for lifting load movements. From the experimentation the angles of each body segment, the effort perceived by the user, and the activation conditions were obtained (Phase 2). After modeling development (Phase 3), the experimental results regarding the force and risk were evaluated obtaining differences between model and experimental data of 0.971 ± 0.171 kg in chest force and 1.983 ± 0.678% in lumbar risk (Phase 4). This approach provides a tool to evaluate the biomechanical effects of this device in a work task, offering a parametric and direct approximation of the effects prior to implementation. Full article
(This article belongs to the Special Issue Wearable Robotics and Assistive Devices)
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19 pages, 5336 KiB  
Article
Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds
by Chiun-Li Chin, Jun-Ren Chen, Wan-Xuan Lin, Hsuan-Chiao Hung, Shang-En Chiang, Chih-Hui Wang, Liang-Ching Lee and Shing-Hong Liu
Sensors 2024, 24(21), 6841; https://doi.org/10.3390/s24216841 - 24 Oct 2024
Viewed by 1187
Abstract
People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance [...] Read more.
People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound–Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road. Full article
(This article belongs to the Special Issue Wearable Robotics and Assistive Devices)
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