ArticleIEEE transactions on medical robotics and bionics2019
Deep generative models with data augmentation to learn robust representations of movement intention for powered leg prostheses.
Article in IEEE transactions on medical robotics and bionics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 32 citations in OpenAlex.
- Adversarial random forests for omics synthesis.bioRxiv : the preprint server for biology · 2026Article
- A Deep Learning Framework With Domain Generalization and Few-Shot Learning for Locomotion Mode Classification Across Users, Sessions, and Prostheses.IEEE transactions on medical robotics and bionics · 2026Article
- Robustification of Bayesian-Inference-Based Gait Estimation for Lower-limb Wearable Robots.IEEE robotics and automation letters · 2024Article
- Improving Amputee Endurance over Activities of Daily Living with a Robotic Knee-Ankle Prosthesis: A Case Study.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2023Article
- Vision-Based Recognition of Human Motion Intent during Staircase Approaching.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2023Article
- Deep-Learning to Map a Benchmark Dataset of Non-amputee Ambulation for Controlling an Open Source Bionic Leg.IEEE robotics and automation letters · 2022Article
- Modeling the Transitional Kinematics Between Variable-Incline Walking and Stair Climbing.IEEE transactions on medical robotics and bionics · 2022Article
- Analysis of the Bayesian Gait-State Estimation Problem for Lower-Limb Wearable Robot Sensor Configurations.IEEE robotics and automation letters · 2022Article
- Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees.Wearable technologies · 2022Article
- Challenges and solutions for application and wider adoption of wearable robots.Wearable technologies · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Intent recognition is a data-driven alternative to expert-crafted rules for triggering transitions between pre-programmed activity modes of a powered leg prosthesis. Movement-related signals from prosthesis sensors detected prior to movement completion are used to predict the upcoming activity. Usually, training data comprised of labeled examples of each activity are necessary; however, the process of collecting a sufficiently large and rich training dataset from an amputee population is tedious. In addition, covariate shift can have detrimental effects on a controller's prediction accuracy if the classifier's learned representation of movement intention is not robust enough. Our objective was to develop and evaluate techniques to learn robust representations of movement intention using data augmentation and deep neural networks. In an offline analysis of data collected from four amputee subjects across three days each, we demonstrate that our approach produced realistic synthetic sensor data that helped reduce error rates when training and testing on different days and different users. Our novel approach introduces an effective and generalizable strategy for augmenting wearable robotics sensor data, challenging a pre-existing notion that rehabilitation robotics can only derive limited benefit from state-of-the-art deep learning techniques typically requiring more vast amounts of data.
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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.