ArticleFrontiers in bioengineering and biotechnology2020
Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2025Article
- Forecasting motion trajectories of elbow and knee joints during infant crawling based on long-short-term memory (LSTM) networks.Biomedical engineering online · 2025Article
- Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed.Scientific reports · 2025Article
- Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- Novel Methods for Personalized Gait Assistance: Three-Dimensional Trajectory Prediction Based on Regression and LSTM Models.Biomimetics (Basel, Switzerland) · 2024Article
- Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning.Medical & biological engineering & computing · 2024Article
- Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System.Sensors (Basel, Switzerland) · 2024Article
- Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence.Frontiers in bioengineering and biotechnology · 2024Article
- Predicting improvement in biofeedback gait training using short-term spectral features from minimum foot clearance data.Frontiers in bioengineering and biotechnology · 2024Article
- A machine learning approach to quantify individual gait responses to ankle exoskeletons.bioRxiv : the preprint server for biology · 2023Article
- Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Treatment Outcome Prediction Using Multi-Task Learning: Application to Botulinum Toxin in Gait Rehabilitation.Sensors (Basel, Switzerland) · 2022Article
- Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.Sensors (Basel, Switzerland) · 2022Article
- Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities.Frontiers in bioengineering and biotechnology · 2022Article
- The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions.Sensors (Basel, Switzerland) · 2021Article
- Article
- Gait Trajectory and Gait Phase Prediction Based on an LSTM Network.Sensors (Basel, Switzerland) · 2020Article
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4 authors.
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Abstract
This study determined whether the kinematics of lower limb trajectories during walking could be extrapolated using long short-term memory (LSTM) neural networks. It was hypothesised that LSTM auto encoders could reliably forecast multiple time-step trajectories of the lower limb kinematics, specifically linear acceleration (LA) and angular velocity (AV). Using 3D motion capture, lower limb position-time coordinates were sampled (100 Hz) from six male participants (age 22 ± 2 years, height 1.77 ± 0.02 m, body mass 82 ± 4 kg) who walked for 10 min at 5 km/h on a 0% gradient motor-driven treadmill. These data were fed into an LSTM model with a sliding window of four kinematic variables with 25 samples or time steps: LA and AV for thigh and shank. The LSTM was tested to forecast five samples (i.e., time steps) of the four kinematic input variables. To attain generalisation, the model was trained on a dataset of 2,665 strides from five participants and evaluated on a test set of 1 stride from a sixth participant. The LSTM model learned the lower limb kinematic trajectories using the training samples and tested for generalisation across participants. The forecasting horizon suggested higher model reliability in predicting earlier future trajectories. The mean absolute error (MAE) was evaluated on each variable across the single tested stride, and for the five-sample forecast, it obtained 0.047 m/s
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