Ankle Joint Dynamic Stiffness in Long-Distance Runners: Effect of Foot Strike and Shoes Features

Garofolini, Alessandro; Taylor, Simon; McLaughlin, Patrick; Mickle, Karen J.; Frigo, C.

doi:10.3390/app9194100

Cited by 13 publications

(10 citation statements)

References 63 publications

(79 reference statements)

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“…Foot’s movement directly affects the lower extremities (i.e. ankle) dynamics and control [ 87 ] as well as the body’s Centre of Mass (COM) movement [ 88 , 89 ].…”

Section: Discussionmentioning

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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“…Foot’s movement directly affects the lower extremities (i.e. ankle) dynamics and control [ 87 ] as well as the body’s Centre of Mass (COM) movement [ 88 , 89 ].…”

Section: Discussionmentioning

confidence: 99%

“…lateral femur medial epicondyle) identified by palpation [ 50 – 52 ]. The static pose was used to calibrate the position and orientation of the lower body skeletal system [ 53 ].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…α indicates meaningful differences. joint stiffness (Garofolini et al, 2019) and leg stiffness (Shih et al, 2019). However, although mechanically sound, using an increased plantarflexed foot strike as an isolated strategy may not be sufficient and functionally limiting.…”

Section: Discussionmentioning

confidence: 99%

A real-time feedback method to reduce loading rate during running: Effect of combining direct and indirect feedback

Garofolini

Oppici

Taylor

2020

Journal of Sports Sciences

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Impact loading plays a key role in the pathophysiology of running-related injuries. Providing real-time feedback may be an effective strategy to reduce impact loading; however, it is currently unclear what an effective training method to help runners achieve a habitual low loading rate is. We subjected 20 healthy non-runners to a structured sequence of direct and indirect biofeedback designed to facilitate broader exploration of neuro-mechanical workspace for potential movement solutions (indirect feedback on cadence and foot-strike angle) and to refine and converge upon an optimal subset of that space to match the task goal (direct feedback on loading rate). While indirect biofeedback on foot-strike angle yielded a lower impact load than providing direct biofeedback on loading rate, compared to indirect biofeedback on foot-strike angle, providing direct feedback on loading rate statistically increased (+58%, p = 0.007) the range of goal-relevant solutions participants used to lower their impact loading. Results showed that structured feedback was effective in increasing the range of input parameters that match the task goal, hence expanding the size of goal-relevant solutions, which may benefit running performance under changing environmental constraints.

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“…Stiffness of the ankle joint calculated using the conventional method has been considered in wide range of studies such as: comparison of AJS in healthy vs. pathological participants [11,12], the influence of AJS on sport performance [10,[13][14][15][16], the role of AJS on the assessment of risk of lower extremity injury [16,17], the reliability of measuring AJS [10,17,18], comparison of the different methods of measuring AJS [8,19], and comparison of leg stiffness against AJS [17]. In addition, AJS calculated using conventional method was used to: compare the variation in AJS with increase in running speed [20], compare the AJS between sprint and endurance athletes during a variety of hopping and drop jump tasks [21], assess the performance between endurance trained athletes and participants from the general population during a maximal hopping test [22], investigate the effect of foot strike and shoe features on ankle joint stiffness [23], and investigate the effects of training on AJS [24]. Hence the previous studies have expressed that AJS could be helpful in the assessment of human locomotion.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Field Measurement of Ankle Joint Stiffness in Hopping

et al. 2021

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Stiffness of ankle joint has been investigated in a wide range of biomechanical studies with a focus on the improvement of performance and reduction in the risk of injury. However, measuring ankle joint stiffness (AJS) using the existing conventional methodologies requires sophisticated equipment such as force plate and motion analyses systems. This study presents a novel method for measuring AJS during a hopping task with no force or motion measurement system. Also the validity of the proposed new method was investigated by comparing the results against those obtained using conventional method in which motion capture and force plate data are used. Twelve participants performed the controlled hopping task at 2.2 Hz, on a force platform, and six high speed cameras recorded the movement. To calculate the AJS in both methods, the lower extremity was modeled as a three linked rigid segments robot with three joints. In the new method, the contact time and flight time were used to calculate ground reaction force, and inverse kinematic and inverse dynamic approaches were used to calculate the ankle kinematic and kinetic. The AJS calculated using the new method was compared against the results of conventional method as the reference. The calculated AJS using this new method (506.47 ± 177.84 N·m/rad) showed a significant correlation (r = 0.752) with the AJS calculated using conventional method (642.39 ± 185.96 N·m/rad). The validation test showed a mean difference of −24.76% using Bland–Altman plot. The presented method can be used as a valid, and low-cost tool for assessing AJS in the field in low resource settings.

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Ankle Joint Dynamic Stiffness in Long-Distance Runners: Effect of Foot Strike and Shoes Features

Cited by 13 publications

References 63 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

A real-time feedback method to reduce loading rate during running: Effect of combining direct and indirect feedback

A Novel Method for Field Measurement of Ankle Joint Stiffness in Hopping

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