ArticleFrontiers in neurorobotics2021
Environment Classification for Robotic Leg Prostheses and Exoskeletons Using Deep Convolutional Neural Networks.
Article in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Diff-MomentFormer: Generative Diffusion-Augmented Transformer for End-to-End Joint Moment Estimation.Sensors (Basel, Switzerland) · 2026Article
- Synthetic Data-Driven Exoskeleton Control via Contralateral Gait Fusion for Variable-Speed Walking.Biomimetics (Basel, Switzerland) · 2026Article
- Ambilateral Activity Recognition and Continuous Adaptation with a Powered Knee-Ankle Prosthesis.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2025Article
- Intelligent prosthetic hips and knees: from actuation to perception and control.Frontiers in neuroscience · 2025Review
- Review on Portable-Powered Lower Limb Exoskeletons.Sensors (Basel, Switzerland) · 2024Review
- Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles.IEEE transactions on bio-medical engineering · 2024Article
- Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots.Biomimetics (Basel, Switzerland) · 2024Article
- StairNet: visual recognition of stairs for human-robot locomotion.Biomedical engineering online · 2024Review
- Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks.IEEE transactions on bio-medical engineering · 2024Article
- L-AVATeD: The lidar and visual walking terrain dataset.Frontiers in robotics and AI · 2024Article
- Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications.Sensors (Basel, Switzerland) · 2023Article
- There are unique kinematics during locomotor transitions between level ground and stair ambulation that persist with increasing stair grade.Scientific reports · 2023Article
- A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision.Scientific data · 2023Article
- A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023Article
- Insight into the Digital Health System of Ukraine (eHealth): Trends, Definitions, Standards, and Legislative Revisions.International journal of telerehabilitation · 2023Article
- Gait variability of outdoor vs treadmill walking with bilateral robotic ankle exoskeletons under proportional myoelectric control.PloS one · 2023Article
- Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning: Predicting Environments From Short-Time Observations of Postural Balance.IEEE transactions on bio-medical engineering · 2022Article
- Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.Sensors (Basel, Switzerland) · 2022Article
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
Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, the current locomotion mode recognition systems being developed for automated high-level control and decision-making rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, we developed an environment classification system powered by computer vision and deep learning to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. In this study, we first reviewed the development of our "ExoNet" database-the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a hierarchical labeling architecture. We then trained and tested over a dozen state-of-the-art deep convolutional neural networks (CNNs) on the ExoNet database for image classification and automatic feature engineering, including: EfficientNetB0, InceptionV3, MobileNet, MobileNetV2, VGG16, VGG19, Xception, ResNet50, ResNet101, ResNet152, DenseNet121, DenseNet169, and DenseNet201. Finally, we quantitatively compared the benchmarked CNN architectures and their environment classification predictions using an operational metric called "NetScore," which balances the image classification accuracy with the computational and memory storage requirements (i.e., important for onboard real-time inference with mobile computing devices). Our comparative analyses showed that the EfficientNetB0 network achieves the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore, which can inform the optimal architecture design or selection depending on the desired performance. Overall, this study provides a large-scale benchmark and reference for next-generation environment classification systems for robotic leg prostheses and exoskeletons.
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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.