ArticleSensors (Basel, Switzerland)2024
Human-in-the-Loop Optimization of Knee Exoskeleton Assistance for Minimizing User's Metabolic and Muscular Effort.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
The trial behind it
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
6 citing papers in PubMed.
- Effects of a Passive Lower-Limb Exoskeleton on Plantar Pressure Distribution and Postural Stability During Prolonged Occupational Standing: A Pilot Ergonomic Study.Healthcare (Basel, Switzerland) · 2026Article
- Design and evaluation of a passive knee-ankle exoskeleton for walking and squatting: a musculoskeletal simulation study.Medical & biological engineering & computing · 2026Article
- EMG-Based Simulation for Optimization of Human-in-the-Loop Control in Simple Robotic Walking Assistance.Journal of sensor and actuator networks · 2025Article
- Optimizing hip exoskeleton assistance pattern based on machine learning and simulation algorithms: a personalized approach to metabolic cost reduction.Frontiers in robotics and AI · 2025Article
- Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton.Sensors (Basel, Switzerland) · 2024Article
- A Novel Personalized Strategy for Hip Joint Flexion Assistance Based on Human Physiological State.Biosensors · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Lower limb exoskeletons have the potential to mitigate work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls adapt an exoskeleton's assistance in real time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton using a CMA-ES algorithm to minimize the users' physical effort, a parameter innovatively evaluated using the interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user's metabolic cost within the HITL control through a machine-learning model. The regression model estimated the metabolic cost, in real time, with a root mean squared error of 0.66 W/kg and mean absolute percentage error of 26% (
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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.