ArticleWearable technologies2022
Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees.
Article in Wearable technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A multimodal EMG-IMU dataset and multi-dataset benchmark for deep learning-based human activity recognition.Scientific reports · 2026Article
- A Task-Agnostic Approach to Unified Multi-Activity Gait Phase Estimation via Bilateral Sensing.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2025Article
- Enhancing Robot Transparency in Human-Robot Prosthesis Interaction to Mitigate Terrain Misrecognition Error.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
- Unified Control of a Powered Knee-Ankle Prosthesis Enables Walking, Stairs, Transitions, and Other Daily Ambulation Activities.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- A Deep Learning Framework for End-to-End Control of Powered Prostheses.IEEE robotics and automation letters · 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
- Using Deep Learning Models to Predict Prosthetic Ankle Torque.Sensors (Basel, Switzerland) · 2023Article
- Generating synthetic gait patterns based on benchmark datasets for controlling prosthetic legs.Journal of neuroengineering and rehabilitation · 2023Article
- Data-efficient human walking speed intent identification.Wearable technologies · 2023Article
- A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs.Frontiers in neurorobotics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Powered prosthetic legs are becoming a promising option for amputee patients. However, developing safe, robust, and intuitive control strategies for powered legs remains one of the greatest challenges. Although a variety of control strategies have been proposed, creating and fine-tuning the system parameters is time-intensive and complicated when more activities need to be restored. In this study, we developed a deep neural network (DNN) model that facilitates seamless and intuitive gait generation and transitions across five ambulation modes: level-ground walking, ascending/descending ramps, and ascending/descending stairs. The combination of latent and time sequence features generated the desired impedance parameters within the ambulation modes and allowed seamless transitions between ambulation modes. The model was applied to the open-source bionic leg and tested on unilateral transfemoral users. It achieved the overall coefficient of determination of 0.72 with the state machine-based impedance parameters in the offline testing session. In addition, users were able to perform in-laboratory ambulation modes with an overall success rate of 96% during the online testing session. The results indicate that the DNN model is a promising candidate for subject-independent and tuning-free prosthetic leg control for transfemoral amputees.
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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.