Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks

Zaroug, Abdelrahman; Lai, Daniel T. H.; Mudie, Kurt; Begg, Rezaul

doi:10.3389/fbioe.2020.00362

Cited by 45 publications

(33 citation statements)

References 54 publications

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“…Recorded 3D positional data were processed using Visual 3D (C-motion, Inc, Version 6) to compute LA and AV for the thigh, shank, and foot segments of the right limb [ 48 ]. LA and AV were then interpolated with a least-squares fit on a 3 rd order polynomial and filtered using a lowpass digital filter with a 15Hz cut-off frequency [ 54 , 55 ].…”

Section: Methodsmentioning

confidence: 99%

“…Timesteps*Features) data structure was transformed into S*T*F (i.e. Samples*Timesteps*Features) structure ( Fig 3 ) [ 48 ]. One sample is a one window that consists of multiple timesteps and the 6 features.…”

Section: Methodsmentioning

confidence: 99%

“…foot AV, shank AV, thigh AV, foot LA, shank LA and thigh LA. In our previous paper [ 48 ], we found that LSTM autoencoder (i.e. LSTM architecture) is able to predict the future gait trajectories at an imposed speed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…RNNs are highly efficient neural networks designed for modeling sequence data such as sentences, voices, and gait patterns. RNNs are naturally more suitable for gait generation tasks than traditional feed-forward neural networks and have been widely used in gait classification [29], [30] and motion forecasting [31], [32].…”

Section: Gait Pattern Generation Modelmentioning

confidence: 99%

Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks

Zhou

Liang

Huang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Individualized reference gait patterns for lower limb rehabilitation robots can greatly improve the effectiveness of rehabilitation. However, previous methods can only generate customized gait patterns at several fixed discrete walking speeds and generating gaits at continuously varying speeds and stride lengths remains unsolved. This work proposes an individualized gait pattern generation method based on a recurrent neural network (RNN), which is proficient in series modeling. We collected the largest gait data set of this kind, which consists of 4,425 gait patterns from 137 subjects. Using this data set, we trained an RNN to create a function mapping from body parameters and gait parameters to a gait pattern. The experimental results indicate that our model is able to generate gait patterns at continuously varying walking speeds and stride lengths while also reducing the errors in the ankle, knee, and hip measurements by 12.83%, 20.95%, and 28.25%, respectively, compared to previous state-of-the-art method.

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“…Liu et al [ 19 ] used a Deep Spatial-Temporal Model to generate a knee joint trajectory one time frame in advance based on the historic gait trajectory of other joints in able-bodied subjects, and applied the new trajectories on a lower-limb exoskeleton for subjects with knee injury. Zaroug et al [ 20 ] used LSTM autoencoders to forecast trajectories of the lower limb kinematics, specifically linear acceleration and angular velocity (AV), and report a correlation coefficient between measured and predicted trajectories of 0.98. Moreira et al [ 21 ] implemented LSTM models to generate healthy reference ankle joint torques of subjects walking on a flat surface, achieving a normalized Root Mean Square Error of 4.31%, showing that the LSTM has the potential to be integrated into control architectures of robotic assistive devices to accurately estimate healthy user-oriented reference ankle joint torques.…”

Section: Introductionmentioning

confidence: 99%

Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

Gutierrez-Farewik

2020

Sensors

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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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Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks

Cited by 45 publications

References 54 publications

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

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

Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks

Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

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