ArticleWearable technologies2020
Systematic framework for performance evaluation of exoskeleton actuators.
Article in Wearable technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human Factor Engineering Research for Rehabilitation Robots: A Systematic Review.Computational intelligence and neuroscience · 2023Pooled it
- Real-time wearable biomechanics framework for sports injury prevention and rehabilitation optimization.Scientific reports · 2026Article
- A compact external-rail linear actuation mechanism with a high stroke-to-length ratio for wearable exoskeleton applications.Frontiers in robotics and AI · 2026Article
- From the idea to the user: a pragmatic multifaceted approach to testing occupational exoskeletons.Wearable technologies · 2025Article
- Wearables for Biomechanical Performance Optimization and Risk Assessment in Industrial and Sports Applications.Bioengineering (Basel, Switzerland) · 2022Review
- Equivalent Weight: Connecting Exoskeleton Effectiveness with Ergonomic Risk during Manual Material Handling.International journal of environmental research and public health · 2021Article
- Versatile and non-versatile occupational back-support exoskeletons: A comparison in laboratory and field studies.Wearable technologies · 2021Article
- Applicability of an Active Back-Support Exoskeleton to Carrying Activities.Frontiers in robotics and AI · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable devices, such as exoskeletons, are becoming increasingly common and are being used mainly for improving motility and daily life autonomy, rehabilitation purposes, and as industrial aids. There are many variables that must be optimized to create an efficient, smoothly operating device. The selection of a suitable actuator is one of these variables, and the actuators are usually sized after studying the kinematic and dynamic characteristics of the target task, combining information from motion tracking, inverse dynamics, and force plates. While this may be a good method for approximate sizing of actuators, a more detailed approach is necessary to fully understand actuator performance, control algorithms or sensing strategies, and their impact on weight, dynamic performance, energy consumption, complexity, and cost. This work describes a learning-based evaluation method to provide this more detailed analysis of an actuation system for our
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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.