Short-Term Effects of the Repeated Exposure to Trip-like Perturbations on Inter-Segment Coordination during Walking: An UCM Analysis

The minimum toe clearance (MTC) results from the coordination of all bilateral lower limb body segments, i.e., a redundant kinematic chain. We tested the hypothesis that repeated exposure to trip-like perturbations induces a more effective covariation of limb segments during steady walking, in accordance with the uncontrolled manifold (UCM) theory, to minimize the MTC across strides. Twelve healthy young adults (mean age 26.2 ± 3.3 years) were enrolled. The experimental protocol consisted of three identical trials, each involving three phases carried outin succession: steady walking (baseline), managing trip-like perturbations, and steady walking (post-perturbation). Lower limb kinematics collected during both steady walking phases wereanalyzed in the framework of the UCM theory to test the hypothesis that the reduced MTC variability following the perturbation can occur, in conjunction with more effective organization of the redundant lower limb segments. Results revealed that, after the perturbation, the synergy underlying lower limb coordination becomes stronger. Accordingly, the short-term effects of the repeated exposure to perturbations modify the organization of the redundant lower limb-related movements. In addition, results confirm that the UCM theory is a promising tool for exploring the effectiveness of interventions aimed at purposely modifying motor behaviors.

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

PLoS ONE ◽

10.1371/journal.pone.0255597 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255597

Author(s):

Abdelrahman Zaroug ◽

Alessandro Garofolini ◽

Daniel T. H. Lai ◽

Kurt Mudie ◽

Rezaul Begg

Keyword(s):

Lower Limb ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Wide Range ◽

Limb Kinematics ◽

Long Short Term Memory ◽

Balance Loss ◽

Lower Limb Kinematics

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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