ArticlePloS one2021
Prediction of gait trajectories based on the Long Short Term Memory neural networks.
Article in PloS one, 2021. 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, 44 citations in OpenAlex.
- Artificial intelligence and sports genomics: advancing precision sports science and athletic performance.Frontiers in sports and active living · 2026Review
- 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
- ADIBAS-Volterra aircraft trajectory prediction model based on improved dynamic integration of beetle tentacle searching.PloS one · 2025Article
- Time Series Classification for Predicting Biped Robot Step Viability.Sensors (Basel, Switzerland) · 2024Article
- Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System.Sensors (Basel, Switzerland) · 2024Article
- Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Using Deep Learning to Predict Minimum Foot-Ground Clearance Event from Toe-Off Kinematics.Sensors (Basel, Switzerland) · 2022Article
- Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.Sensors (Basel, Switzerland) · 2022Article
- Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review.Sensors (Basel, Switzerland) · 2022Review
- Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82-5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.
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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.