ArticleCyborg and bionic systems (Washington, D.C.)2025
Level-Ground and Stair Adaptation for Hip Exoskeletons Based on Continuous Locomotion Mode Perception.
Article in Cyborg and bionic systems (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Assessing Transparency of Robots, Exoskeletons, and Assistive Devices: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Bioinspired Computation for Identifying Joint Compliance in Biomimetic Flexible Manipulators.Biomimetics (Basel, Switzerland) · 2026Article
- Design and Experimental Evaluation of a Shoulder Assistive Exoskeleton for Insulator Replacement.Sensors (Basel, Switzerland) · 2026Article
- A Single-Source Dual-Drive Flexible Knee Assistive Exoskeleton for Elderly Daily Locomotion.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Therapeutic Benefits of Robotics and Exoskeletons for Gait and Postural Balance Among Children and Adolescents with Cerebral Palsy: An Overview of Systematic Reviews.Healthcare (Basel, Switzerland) · 2025Review
- A Survey on Design and Control Methodologies of High- Torque-Density Joints for Compliant Lower-Limb Exoskeleton.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hip exoskeleton can provide assistance to users to augment movements in different scenarios. The assistive control for hip exoskeleton involves the interactions among exoskeleton, user, and environment, which depends on the environment perception (to predict locomotion) to design control strategy combined with gait mode and so on. Current exoskeleton control still needs to be improved in adaptation to continuous locomotion mode and different users. To address this problem, we have employed a learning-free (i.e., non-data-driven) environment perception method to improve hip exoskeleton adaptive control toward continuous locomotion mode. The adaptive control experiments were conducted on level ground and stairs on 7 subjects. The prediction accuracy for steady locomotion mode was more than 95% for each subject (ranged from 95.7% to 99.7%). The prediction accuracy for each locomotion mode transition ranged from 87.5% to 100%, and the transition timing could be detected before the end of transition period. Compared with learning-based (data-driven) approaches, our method achieves better performances in adaptive control for hip exoskeleton and shows some generalization for subjects.
Identifiers
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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.