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Industrial workplaces expose workers to a high risk of injuries such as Work-related Musculoskeletal Disorders (WMSDs). Exoskeletons are wearable robotic technologies that can be used to reduce the loads exerted on the body's joints and reduce the occurrence of WMSDs. However, current studies show that the deployment of industrial exoskeletons is still limited, and widespread adoption depends on different factors, including efficacy evaluation metrics, target tasks, and supported body postures. Given that exoskeletons are not yet adopted to their full potential, we propose a review based on these three evaluation dimensions that guides researchers and practitioners in properly evaluating and selecting exoskeletons and using them effectively in workplaces. Specifically, evaluating an exoskeleton needs to incorporate: (1) efficacy evaluation metrics based on both subjective (e.g., user perception) and objective (e.g., physiological measurements from sensors) measures, (2) target tasks (e.g., manual material handling and the use of tools), and (3) the body postures adopted (e.g., squatting and stooping). This framework is meant to guide the implementation and assessment of exoskeletons and provide recommendations addressing potential challenges in the adoption of industrial exoskeletons. The ultimate goal is to use the framework to enhance the acceptance and adoption of exoskeletons and to minimize future WMSDs in industrial workplaces.
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http://dx.doi.org/10.3390/s22072714 | DOI Listing |
Comput Methods Biomech Biomed Engin
September 2025
Centre for Robotics and Intelligent Systems Research, Institution of Ningbo Industrial Internet Research, Ningbo, China.
Aiming at the problems of low accuracy and poor robustness in gait recognition of lower extremity exoskeleton robots in human-computer interaction, a depth residual contraction network recognition method based on the fusion of surface electrosemg (sEMG) and inertial measurement unit (IMU) signals was proposed. Firstly, a new energy kernel feature extraction method was used to extract sEMG signals. Based on the sEMG oscillator model, the sEMG energy kernel phase diagram was converted to gray level map by matrix counting method.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Department of Civil Engineering, School of Engineering, Aalto University, P.O. Box 12100, FI-00076 Aalto, Finland.
Innovative technologies have been helping to improve comfort and safety at work in high-risk sectors for years. The study analysed the impact, along with an assessment of potential implementations (opportunities and limitations) of innovative technological solutions for improving occupational safety in two selected sectors of the economy: mining and construction. The technologies evaluated included unmanned aerial vehicles and inspection robots, the Internet of Things and sensors, artificial intelligence, virtual and augmented reality, innovative individual and collective protective equipment, and exoskeletons.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Department of Mechanical Systems Engineering, Tokyo Metropolitan University, Tokyo 191-0065, Japan.
The half-sitting posture is essential for many functional tasks performed by industrial workers. Thus, passive lower-limb exoskeletons, known as wearable chairs, are increasingly used to relieve lower-limb loading in such scenarios. However, although these devices lighten muscle effort during half-sitting tasks, they can disrupt walking mechanics and balance.
View Article and Find Full Text PDFJ Neuroeng Rehabil
August 2025
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Milan, Italy.
Background: Multiple Sclerosis (MS) is a neurodegenerative disorder causing lower and upper-limb (UL) impairments and significantly affecting independence. Current assistive technologies for UL rehabilitation in People with MS (PwMS) rely on actuated robotic systems, which present high costs and complexity. Passive gravity-compensated exoskeletons represent a promising alternative; however, their functional benefits remain underexplored.
View Article and Find Full Text PDFIEEE Trans Med Robot Bionics
May 2025
Department of Mechanical Engineering and the Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, 30332 USA.
Fall incidents due to slips are some of the most common causes of injuries for industry workers and older adults, motivating research to assist balance recovery following slips. To assist balance recovery during a slip, a detection algorithm that can work with an assistive device, such as an exoskeleton, needs to be able to detect slips rapidly after onset, which remains a critical gap in the field. Here, we compared the ability of linear discriminant analysis (LDA), extreme gradient boosting (XGBoost), and convolutional neural networks (CNN) to detect slip using only native sensors on a hip exoskeleton.
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