Prediction of gait trajectories based on the Long Short Term Memory neural networks

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Zaroug, Abdelrahman ORCID: 0000-0003-1716-2914, Garofolini, Alessandro ORCID: 0000-0002-1789-2362, Lai, Daniel ORCID: 0000-0003-3459-7709, Mudie, Kurt ORCID: 0000-0001-9328-4759 and Begg, Rezaul ORCID: 0000-0002-3195-8591 (2021) Prediction of gait trajectories based on the Long Short Term Memory neural networks. PLoS One, 16 (8). ISSN 1932-6203

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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Item type Article
URI https://vuir.vu.edu.au/id/eprint/42512
DOI https://doi.org/10.1371/journal.pone.0255597
Official URL https://journals.plos.org/plosone/article?id=10.13...
Subjects Current > FOR (2020) Classification > 4207 Sports science and exercise
Current > Division/Research > Institute for Health and Sport
Keywords prevention of falls, fractures, risks, balance, rehabilitation
Citations in Scopus 0 - View on Scopus
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