ArticleWearable technologies2023
Evaluation of a machine-learning-driven active-passive upper-limb exoskeleton robot: Experimental human-in-the-loop study.
Article in Wearable technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Estimation of walking energy expenditure using a single sacrum-mounted IMU based on biomechanically-informed machine learning.Scientific reports · 2025Article
- Review of sEMG for Exoskeleton Robots: Motion Intention Recognition Techniques and Applications.Sensors (Basel, Switzerland) · 2025Review
- The perceptual and biomechanical effects of scaling back exosuit assistance to changing task demands.Scientific reports · 2025Article
- Performance characterization of a novel semi-active exoskeleton for overhead work.Wearable technologies · 2025Article
- Mapping the evidence on occupational exoskeleton use for the workforce in healthcare, social care, and industry: A systematic scoping review.Wearable technologies · 2025Review
- Joint coordination constraints using an upper limb exoskeleton impact novel skill acquisition.Wearable technologies · 2025Article
- Intra-tester and inter-tester reliability of maximum isokinetic hip and knee strength measurements with the exoskeleton in healthy young adults.Frontiers in sports and active living · 2025Article
- Design and evaluation of AE4W: An active and flexible shaft-driven shoulder exoskeleton for workers.Wearable technologies · 2025Article
- Design, development, and evaluation of a novel shoulder phantom testbed for analyzing interaction forces and reachability of wearable exoskeletons.Wearable technologies · 2025Article
- Experimental Study of Fully Passive, Fully Active, and Active-Passive Upper-Limb Exoskeleton Efficiency: An Assessment of Lifting Tasks.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evaluating exoskeleton actuation methods and designing an effective controller for these exoskeletons are both challenging and time-consuming tasks. This is largely due to the complicated human-robot interactions, the selection of sensors and actuators, electrical/command connection issues, and communication delays. In this research, a test framework for evaluating a new active-passive shoulder exoskeleton was developed, and a surface electromyography (sEMG)-based human-robot cooperative control method was created to execute the wearer's movement intentions. The hierarchical control used sEMG-based intention estimation, mid-level strength regulation, and low-level actuator control. It was then applied to shoulder joint elevation experiments to verify the exoskeleton controller's effectiveness. The active-passive assistance was compared with fully passive and fully active exoskeleton control using the following criteria: (1) post-test survey, (2) load tolerance duration, and (3) computed human torque, power, and metabolic energy expenditure using sEMG signals and inverse dynamic simulation. The experimental outcomes showed that active-passive exoskeletons required less muscular activation torque (50%) from the user and reduced fatigue duration indicators by a factor of 3, compared to fully passive ones.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.