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Recent Trends and Advances in Wearable Exoskeletons, Sensing Technologies and Occupational Ergonomics

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 5205

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Special Issue Editors

Prof. Dr. Yong-Ku Kong

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Guest Editor

Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: physical ergonomics; wearable exoskeleton system; biomechanics
Special Issues, Collections and Topics in MDPI journals

Dr. Jaehyun Park

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Guest Editor

Department of Industrial Engineering, Konkuk University, Seoul 05029, Republic of Korea
Interests: physical ergonomics; biomechanics; exoskeletons; wearable sensors
Special Issues, Collections and Topics in MDPI journals

Dr. Jeong-ho Kim

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Guest Editor

Department of Environmental and Occupational Health, School of Public Health, Texas A&M University, College Station, TX 77840, USA
Interests: HCI; user experience; biomechanics

Special Issue Information

Dear Colleagues,

We are pleased to propose a Special Issue of Sensors titled "Recent Trends and Advances in Wearable Exoskeletons, Sensing Technologies and Occupational Ergonomics". Work-related musculoskeletal disorders (WMSDs) have emerged as an important issue on industrial sites and in physical ergonomics research due to growing interest in occupational health and the creation of safer working environments, as well as shifting perceptions of labor across various industries. Addressing these concerns requires an in-depth understanding of the interaction between physical and cognitive ergonomics, sensor-driven data analysis, and the fields of occupational safety and health. To enhance the safety and usability of industrial environments for a wide range of workers and product users, it is essential to conduct comprehensive research on these interdisciplinary interactions, leveraging sensing capabilities to capture critical human–machine and environmental data.

In recent years, the development and usability evaluation of wearable exoskeletons integrated with sensors to monitor movement, force, and physiological signals—aimed at preventing musculoskeletal disorders among industrial workers and rehabilitation professionals—have gained significant global traction. This research topic, enhanced by sensing innovations, has emerged as one of the most important areas within the field of physical ergonomics. Accordingly, this Special Issue will focus primarily on research into wearable exoskeletons for the upper limbs, lower back, and lower limbs, alongside advancements in sensing technologies that enable their optimization. However, its scope is not limited to exoskeleton-related studies alone; we also welcome submissions from broader domains, including occupational ergonomics, safety, and health, particularly those incorporating sensing-based approaches, with the goal of fostering interdisciplinary collaboration and the sharing of diverse research outcomes.

Prof. Dr. Yong-Ku Kong
Dr. Jaehyun Park
Dr. Jeong-ho Kim
Guest Editors

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Keywords

wearable exoskeleton
industrial wearable robot
work-related musculoskeletal disorders (WMSDs)
physical ergonomics
occupational ergonomics
human–machine interaction
aging study

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Published Papers (4 papers)

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Research

36 pages, 6596 KB

Open AccessArticle

Co-Design of Smartphone- and Smartwatch-Based Occupational Health Visualisations in Office Environments

by Phillip Probst, Sara Santos, Gonçalo Barros, Mariana Morais, Sofia Garcia, Philipp Koch, Jorge Barroso Dias, Ana Leal, Rute Periquito, Sofia André, Tiago Matoso, Cristina Pinho, Ricardo Vigário and Hugo Gamboa

Sensors 2026, 26(7), 2278; https://doi.org/10.3390/s26072278 - 7 Apr 2026

Viewed by 615

Abstract

Office workers are exposed to a range of occupational health risks, including prolonged sedentary behaviour, postural load, elevated heart rate, and noise, yet objective and continuous monitoring of these risk factors in workplace settings remains uncommon. This study aimed to co-design occupational health visualisations based on smartphone and smartwatch data, through a multi-stakeholder group of office workers and occupational health professionals. A generative co-design framework was applied, comprising a pre-design phase with a field study and questionnaire, a structured multi-stakeholder workshop, and a follow-up evaluation session. Thematic analysis of the workshop transcript yielded 17 occupational health themes, which were subsequently assessed for technical feasibility relative to the available sensing platform. Of the 27 discrete visualisation elements proposed across both groups, the majority were classified as directly addressable using smartphone and smartwatch sensor data. Visualisations covering physical activity, heart rate, environmental noise exposure, and postural load were implemented in Python using real-world data collected from office workers. The follow-up session provided qualitative confirmation that the developed visualisations were interpretable and aligned with the stakeholder expectations. The generative co-design framework proved well-suited to the occupational health visualisation context, enabling structured translation of stakeholder requirements into technically feasible and interpretable visualisation outputs. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Wearable Exoskeletons, Sensing Technologies and Occupational Ergonomics)

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27 pages, 18163 KB

Open AccessArticle

Evaluation of Different Controllers for Sensing-Based Movement Intention Estimation and Safe Tracking in a Simulated LSTM Network-Based Elbow Exoskeleton Robot

by Farshad Shakeriaski and Masoud Mohammadian

Sensors 2026, 26(2), 387; https://doi.org/10.3390/s26020387 - 7 Jan 2026

Viewed by 894

Abstract

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Wearable Exoskeletons, Sensing Technologies and Occupational Ergonomics)

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28 pages, 4782 KB

Open AccessArticle

Computer Simulation of Whole-Body Vibration in Port Container Handling Machine Operators

by Ricardo Luís Alves Silva, Kleber Gonçalves Alves, José Ângelo Peixoto da Costa, Alvaro Antonio Villa Ochoa, Roberto Nobuyoshi Junior Yamada, Paula Suemy Arruda Michima, Gustavo de Novaes Pires Leite and Álvaro Augusto Soares Lima

Sensors 2025, 25(20), 6346; https://doi.org/10.3390/s25206346 - 14 Oct 2025

Viewed by 1461

Abstract

This study aimed to evaluate the effect of whole-body vibrations (WBV) on ergonomics related to static posture during the operation of container handling machines (Portainer). A 3D numerical model of a seated man was developed using modal and harmonic analysis based on the finite element method (FEM), and implemented on the ANSYS platform to achieve this. Quantitative analyses of whole-body vibrations were carried out in actual workplaces at a port terminal in northeastern Brazil, considering the interaction between the human and the machine. A comparison was made between the real data collected at the operating sites and the values obtained from the developed model. Concerning vibration exposure, the results revealed a critical situation: in 86.2% of cases, the Acceleration of Resulting Normalized Exposure—A(8)—exceeded the alert level, and in 96.6% of cases, the Resulting Vibration Dose Value (VDV) also surpassed this threshold. Similarly, an alert level was exceeded in 97.0% of cases. According to the maximum limits established by Brazilian legislation, the acceleration from normalized exposure did not exceed the limit, while the resulting vibration dose value was surpassed in 20% of cases. The modal analysis results helped identify the critical directions of vibration response, thus supporting the assessment of human exposure effects and the structural performance of the system analyzed. The harmonic analysis showed good agreement between the model and the real acceleration data in the frequency range of 3 to 4 Hz. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Wearable Exoskeletons, Sensing Technologies and Occupational Ergonomics)

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15 pages, 2303 KB

Open AccessArticle

Center of Pressure Analysis of Postural Stability During Repetitive Reaching with Passive Arm-Support Exoskeletons

by Byungkyu Choi and Jaehyun Park

Sensors 2025, 25(18), 5650; https://doi.org/10.3390/s25185650 - 10 Sep 2025

Cited by 2 | Viewed by 1521

Abstract

This study assessed the effects of passive arm-support exoskeletons (ASEs) on postural stability during repetitive arm-reaching tasks. In a 3 × 3 × 2 within-subject design, twenty-four healthy right-handed men completed left-, front-, and right-facing arm-reaching tasks at two working distances (65.5 and 68.9 cm) under three intervention conditions (Without, VEX, Airframe). Postural stability was assessed using center of pressure (CoP) data recorded from a force plate. Both ASEs clearly reduced the mean amplitude of CoP in the mediolateral (ML) direction (i.e., the absolute value of MEAN ML and ML APDF10), although neither yielded improvements in anteroposterior (AP) stability. Task direction significantly influenced all CoP measures: left-facing tasks produced the greatest leftward bias, whereas front-facing tasks yielded the smallest AP CoP amplitude. Increasing the working distance by <4 cm modestly heightened AP bias, as reflected in larger AP bias metrics (i.e., MEAN AP, ML APDF50, and ML APDF90). Overall, passive ASEs selectively enhanced lateral postural control, while their effect on AP stability was negligible or even slightly adverse. These findings indicate that the practical utility of passive ASEs depends on the directional demands of specific occupational tasks. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Wearable Exoskeletons, Sensing Technologies and Occupational Ergonomics)

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