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

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

doi:10.1371/journal.pone.0255597

Cited by 20 publications

(22 citation statements)

References 85 publications

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“…In order to classify the lifting motion, the following three machine learning techniques commonly used as classification models in previous studies [32,33] were used: ANN, Support Vector Machine (SVM), Random Forest (RF). The LSTM model showing relatively high performance on a time-series data set [34][35][36][37] was used as a regression model for predicting joint moment. Figure 1 presents the structure of the proposed architecture and the overall flow chart to predict lower extremities and L5/S1 moments on sagittal plane.…”

Section: Neural Network Architecturementioning

confidence: 99%

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

et al. 2021

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Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06~13.88% while the single LSTM showed 9.30~18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.

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Section: Neural Network Architecturementioning

confidence: 99%

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

et al. 2021

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“…From Figure 2 , only simple mathematical operations are performed to update the memory cell in each LSTM cell. The LSTM layers can be stacked to have the means for finely extracting intermediate features and finding temporal patterns from low-level input features ( Zaroug et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…They concluded that the LSTM model showed an improved performance over linear regression and also Dense Neural Networks (DNN) for an ankle angle prediction task. Zaroug et al (2021) investigated the applicability of LSTM for the prediction of angular velocity and linear acceleration in the sagittal plane of thigh, shank and foot segments. Inspired by the Sequence to Sequence (Seq2Seq) model in the Natural Language Process (NLP) field, they adopted a two stage Encoder-Decoder LSTM model ( Zaroug et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the Sequence to Sequence (Seq2Seq) model in the Natural Language Process (NLP) field, they adopted a two stage Encoder-Decoder LSTM model ( Zaroug et al, 2020 ). Other research has shown that Bidirectional LSTM (Bi-LSTM) provides a modest accuracy increase for human gait cycle judgement ( Lee et al, 2021 ; Zaroug et al, 2021 ; Hong et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Ding

Plummer²,

Georgilas³

2022

Front. Bioeng. Biotechnol.

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Reliable estimation of desired motion trajectories plays a crucial part in the continuous control of lower extremity assistance devices such as prostheses and orthoses. Moreover, reliable estimation methods are also required to predict hard-to-measure biomechanical quantities (e.g., joint contact moment/force) for use in sports injury science. Recognising that human locomotion is an inherently time-sequential and limb-synergetic behaviour, this study investigates models and learning algorithms for predicting the motion of a subject’s leg from the motion of complementary limbs. The novel deep learning model architectures proposed are based on the Long Short-Term Memory approach with the addition of an attention mechanism. A dataset comprising Inertial Measurement Unit signals from 21 subjects traversing varied terrains was used, including stair ascent/descent, ramp ascent/descent, stopped, level-ground walking and the transitions between these conditions. Fourier Analysis is deployed to evaluate the model robustness, in addition to assessing time-based prediction errors. The experiment on three unseen test participants suggests that the branched neural network structure is preferred to tackle the multioutput problem, and the inclusion of an attention mechanism demonstrates improved performance in terms of accuracy, robustness and network size. An experimental comparison found that 57% of the model parameters were not needed after adding attention layers meanwhile the prediction error is lower than the LSTM model without attention mechanism. The attention model has errors of 9.06% and 7.64% (normalised root mean square error) for ankle and hip acceleration prediction respectively. Also, less high-frequency noise is present in the attention model predictions. We conclude that the internal structure of the proposed deep learning model is justified, principally the benefit of using an attention mechanism. Experimental results for biomechanical motion estimation are obtained, showing greater accuracy than only with LSTM. The trained attention model can be used throughout despite transitioning between terrain types. Such a model will be useful in, for example, the control of lower-limb prostheses, instead of the need to identify and switch between different trajectory generators for different walking modes.

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“…Therefore, new technologies such as artificial intelligence (AI), along with biosignal processing and neural technology, will be extremely important for the development of future exoskeleton research [ 249 ]. Thanks to corrective and predictive machine learning algorithms [ 216 , 250 ], IMU sensors seem to be a very promising replacement for a wide range of physical sensors such as FSRs, pressure insoles, footswitches, potentiometers, and encoders, as they are shown to be capable of both detecting the gait phases and measuring the limb orientation and movement. The magnetometer and accelerometer components in IMU sensors might also become useful in the near future for evaluating muscle activities without EMG electrodes [ 251 ].…”

Section: Towards Fully Autonomous Portable Paesmentioning

confidence: 99%

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

kian

Widanapathirana

Joseph

et al. 2022

Sensors

Self Cite

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Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

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

Cited by 20 publications

References 85 publications

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

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