ArticleIEEE transactions on bio-medical engineering2022
Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 27 citations in OpenAlex.
- 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
- Real-time Adaptation of Deep Learning Walking Speed Estimators Enables Biomimetic Assistance Modulation in an Open-Source Bionic Leg.IEEE transactions on medical robotics and bionics · 2025Article
- 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
- Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.IEEE robotics and automation letters · 2024Article
- Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks.IEEE transactions on bio-medical engineering · 2024Article
- Controlling Powered Prosthesis Kinematics Over Continuous Inter-Leg Transitions Between Walking and Stair Ascent/Descent.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Controlling Powered Prosthesis Kinematics over Continuous Transitions Between Walk and Stair Ascent.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 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
- Adaptive Lower Limb Pattern Recognition for Multi-Day Control.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveWe evaluated a two-step method to improve control accuracy for a powered prosthetic leg using machine learning and adaptation, while reducing subject training time.
methodsFirst, information from three transfemoral amputees was grouped together, to create a baseline control system that was subsequently tested using data from a fourth subject (user-independent classification). Second, online adaptation was investigated, whereby the fourth subject's data were used to improve the baseline control system in real-time. Results were compared for user-independent classification and for user-dependent classification (data collected from and tested in the same subject), with and without adaptation.
resultsThe combination of a user-independent classifier with real-time adaptation based on a unique individual's data produced a classification error rate as low as 1.61% [0.15 standard error of the mean (SEM)] without requiring collection of initial training data from that individual. Training/testing using a subject's own data (user-dependent classification), combined with adaptation, resulted in a classification error rate of 0.9% [0.12 SEM], which was not significantly different (P 0.05) but required, on average, an additional 7.52 hours [0.92 SEM] of training sessions. CONCLUSION AND SIGNIFICANCE: We found that the combination of a user-independent dataset with adaptation resulted in error rates that were not significantly different from using a user-dependent dataset. Furthermore, this method eliminated the need for individual training sessions, saving many hours of data collection time.
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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.