ArticleIEEE robotics and automation letters2021
Real-Time Gait Phase Estimation for Robotic Hip Exoskeleton Control During Multimodal Locomotion.
Article in IEEE robotics and automation letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 151 citations in OpenAlex.
- Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Gait-Semantic Relational Knowledge Distillation for Ground Reaction Force Estimation.Advanced engineering informatics · 2026Article
- Embedded Adaptive Admittance Control for a Modular Hip Exoskeleton Using Deep Learning-Based Gait-Phase Estimation.Sensors (Basel, Switzerland) · 2026Article
- Synthetic Data-Driven Exoskeleton Control via Contralateral Gait Fusion for Variable-Speed Walking.Biomimetics (Basel, Switzerland) · 2026Article
- Long short-term attention memory (LSTAM): a global-feature-integrated model for joint moment prediction in human rehabilitation.Scientific reports · 2026Article
- Gait Event Detection From Thigh Segment Kinematics in Children With Crouch Gait.IEEE journal of translational engineering in health and medicine · 2026Article
- Hip exoskeleton assistance with machine-learning-based state estimation improves gait kinematics of people with Parkinson's disease.Frontiers in robotics and AI · 2026Article
- Real-Time Balancing of Stability and Plasticity in Continual Learning Enables Adaptive Speed Estimation for Lower-Limb Prostheses.IEEE transactions on medical robotics and bionics · 2025Article
- Orthopedic robotic surgery: multiple analytical approaches and a bibliometric study of the top 100 most cited papers.Journal of robotic surgery · 2025Article
- Clinical trials of GPE-based muscle support algorithm for robotic hip exoskeleton: a pilot study.Scientific reports · 2025Article
- NeuroSkinSensors (Basel, Switzerland) · 2025Article
- Machine Learning Enables Rapid Detection of Slips Using a Robotic Hip Exoskeleton.IEEE transactions on medical robotics and bionics · 2025Article
- A Task-Agnostic Approach to Unified Multi-Activity Gait Phase Estimation via Bilateral Sensing.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2025Article
- A Task-Agnostic Hip Exoskeleton for Osteoarthritis Pain Relief: Energetic Control Across Activities of Daily Life.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2025Article
- Learning based lower limb joint kinematic estimation using open source IMU data.Scientific reports · 2025Article
- Article
- Online Adaptation Framework Enables Personalization of Exoskeleton Assistance During Locomotion in Patients Affected by Stroke.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2025Article
- AI-driven universal lower-limb exoskeleton system for community ambulation.Science advances · 2024Article
- The Design and Application of an Assistive Hip Joint Exoskeleton for Tower Climbing.Sensors (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
We developed and validated a gait phase estimator for real-time control of a robotic hip exoskeleton during multimodal locomotion. Gait phase describes the fraction of time passed since the previous gait event, such as heel strike, and is a promising framework for appropriately applying exoskeleton assistance during cyclic tasks. A conventional method utilizes a mechanical sensor to detect a gait event and uses the time since the last gait event to linearly interpolate the current gait phase. While this approach may work well for constant treadmill walking, it shows poor performance when translated to overground situations where the user may change walking speed and locomotion modes dynamically. To tackle these challenges, we utilized a convolutional neural network-based gait phase estimator that can adapt to different locomotion mode settings to modulate the exoskeleton assistance. Our resulting model accurately predicted the gait phase during multimodal locomotion without any additional information about the user's locomotion mode, with a gait phase estimation RMSE of 5.04 ± 0.79%, significantly outperforming the literature standard (
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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.