ArticleSensors (Basel, Switzerland)2019
Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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
14 citing papers in PubMed.
- Locomotion Decoding (Applied bionics and biomechanics · 2026Article
- Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration.Journal of neuroengineering and rehabilitation · 2025Article
- Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification.Sensors (Basel, Switzerland) · 2024Article
- Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots.Biomimetics (Basel, Switzerland) · 2024Article
- Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review.IEEE transactions on medical robotics and bionics · 2023Article
- Recognition of Upper Limb Action Intention Based on IMU.Sensors (Basel, Switzerland) · 2022Article
- Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.Frontiers in physiology · 2022Article
- Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- Challenges and solutions for application and wider adoption of wearable robots.Wearable technologies · 2021Review
- Environment Classification for Robotic Leg Prostheses and Exoskeletons Using Deep Convolutional Neural Networks.Frontiers in neurorobotics · 2021Article
- Sensor Fusion in Assistive and Rehabilitation Robotics.Sensors (Basel, Switzerland) · 2020Article
- Relying on more sense for enhancing lower limb prostheses control: a review.Journal of neuroengineering and rehabilitation · 2020Review
- ExoNet Database: Wearable Camera Images of Human Locomotion Environments.Frontiers in robotics and AI · 2020Article
- A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices.Sensors (Basel, Switzerland) · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately these predictions can have substantial errors, which can potentially lead to trips or falls. It is hypothesized that one reason for the significant prediction errors in the current control systems for powered lower-limb prostheses is due to the inter- and intra-subject variability of the data sources used for prediction. Environmental data, recorded from a depth sensor worn on a belt, should have less variability across trials and subjects as compared to kinetics, kinematics and EMG data, and thus its addition is proposed. The variability of each data source was analyzed, once normalized, to determine the intra-activity and intra-subject variability for each sensor modality. Then measures of separability, repeatability, clustering and overall desirability were computed. Results showed that combining Vision, EMG, IMU (inertial measurement unit), and Goniometer features yielded the best separability, repeatability, clustering and desirability across subjects and activities. This will likely be useful for future application in a forward predictor, which will incorporate Vision-based environmental data into a forward predictor for powered lower-limb prosthesis 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.