Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

Abstract

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.

Kinematics, Speed, and Anthropometry-Based Ankle Joint Torque Estimation: A Deep Learning Regression Approach

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Study of the Distortion of the Indirect Angular Measurements of the Calcaneus Due to Perspective: In Vitro Testing

“…Next, these differences in the stance phase’s subphases are explained, namely, loading response, mid-stance, terminal stance, and pre-swing [ 31 , 32 , 33 , 34 ].…”

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

“…bad strides) were labelled as bad and excluded from the final time series data. The final time series data were downsampled to 50 Hz (to accelerate LSTM computational time) [ 43 ] and normalised with z-scores using Matlab (Mathworks, Inc, R2020a). The sagittal plane kinematics included the translation along the Y-axis (i.e.…”

“…The LSTM neural networks are an ANN architecture known for modelling time-series information [ 41 , 42 ]. The LSTM neural networks have proven wide success in modelling human movement data such as the lower limb kinematics prediction [ 43 ] neurodegenerative disease diagnosis [ 44 ], gait event detection [ 45 , 46 ] and falls recognition [ 47 ]. The aim of this work was to develop and compare four standard LSTM architectures (Vanilla, Stacked, Bidirectional and Auto-encoders) for the prediction of future lower limb trajectories, i.e.…”