ArticleSensors (Basel, Switzerland)2020
Gait Trajectory and Gait Phase Prediction Based on an LSTM Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The trial behind it
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Who cites it
21 citing papers in PubMed, 85 citations in OpenAlex.
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- Forecasting motion trajectories of elbow and knee joints during infant crawling based on long-short-term memory (LSTM) networks.Biomedical engineering online · 2025Article
- Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- Gait pattern modification based on ground contact adaptation using the robot-assisted training platform (RATP).Medical & biological engineering & computing · 2025Article
- Transfer learning-enhanced CNN-GRU-attention model for knee joint torque prediction.Frontiers in bioengineering and biotechnology · 2025Article
- A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition.Sensors (Basel, Switzerland) · 2024Article
- Phase-Based Gait Prediction after Botulinum Toxin Treatment Using Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Novel Methods for Personalized Gait Assistance: Three-Dimensional Trajectory Prediction Based on Regression and LSTM Models.Biomimetics (Basel, Switzerland) · 2024Article
- Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System.Sensors (Basel, Switzerland) · 2024Article
- Design, Analysis, and Development of Low-Cost State-of-the-Art Magnetorheological-Based Microprocessor Prosthetic Knee.Sensors (Basel, Switzerland) · 2024Article
- A Novel Prediction Method of Transfer-Assisted Action Oriented to Individual Differences for the Excretion Care Robot.Sensors (Basel, Switzerland) · 2023Article
- A machine learning approach to quantify individual gait responses to ankle exoskeletons.bioRxiv : the preprint server for biology · 2023Article
- Treatment Outcome Prediction Using Multi-Task Learning: Application to Botulinum Toxin in Gait Rehabilitation.Sensors (Basel, Switzerland) · 2022Article
- Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.Sensors (Basel, Switzerland) · 2022Article
- [Research on gait recognition and prediction based on optimized machine learning algorithm].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications.Sensors (Basel, Switzerland) · 2021Article
- Study of the Distortion of the Indirect Angular Measurements of the Calcaneus Due to Perspective: In Vitro Testing.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases.
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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.