Defining gait patterns using Parallel Factor 2 (PARAFAC2): A new analysis of previously published data

Liew, Bernard X. W.; Morris, Susan; Netto, Kevin

doi:10.1016/j.jbiomech.2019.04.035

Cited by 5 publications

(7 citation statements)

References 26 publications

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“…These participants had simultaneous bilateral foot contacts on the same force plate, resulting in an absence of consecutive good foot contact strides which lasted >50% of the walking duration. The 50% threshold was determined by the authors to minimize manual identification of foot contact events, to increase processing replicability ( Liew et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…The associated absolute walking speeds for all eight conditions for each participant were reported by the authors ( Fukuchi et al, 2018 ). Marker trajectories and ground reaction force (GRF) were low passed filtered at a matched frequency of 6 Hz (4th Order, zero-lag, Butterworth) ( Liew et al, 2019 ). A seven-segment lower limb, 6DOF joint model was developed in Visual 3D software (C-motion Inc., Germantown, MD, United States) ( Liew et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

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Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning

Boukhennoufa

Altai

Zhai

et al. 2022

Front. Bioeng. Biotechnol.

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Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants n = 33). The outcome for prediction was the internal knee abduction impulse over the stance phase. Three-dimensional (3D) angular and linear displacement, velocity, and acceleration of the seven lower body segment’s center of mass (COM), relative to a fixed global coordinate system were derived and formed the predictor space (126 time-series predictors). The total number of observations in the dataset was 6,737. The datasets were split into training (75%, n = 5,052) and testing (25%, n = 1685) datasets. Five deep learning models were benchmarked against inverse dynamics in quantifying knee abduction impulse. A baseline 2D convolutional network model achieved a mean absolute percentage error (MAPE) of 10.80%. Transfer learning with InceptionTime was the best performing model, achieving the best MAPE of 8.28%. Encoding the time-series as images then using a 2D convolutional model performed worse than the baseline model with a MAPE of 16.17%. Time-series based deep learning models were superior to an image-based method when predicting knee abduction moment impulse during walking. Future studies looking to develop wearable technologies will benefit from knowing the optimal network architecture, and the benefit of transfer learning for predicting joint moments.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning

Boukhennoufa

Altai

Zhai

et al. 2022

Front. Bioeng. Biotechnol.

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Section: Designmentioning

confidence: 99%

“…Only data from the speed condition of "100%" were extracted from the present analysis. Marker trajectories and ground reaction force (GRF) were low passed filtered at a matched frequency of 6Hz (4 th Order, zero-lag, Butterworth) [27]. A seven segment lower limb, 6DOF joint model was developed in Visual 3D software (C-motion Inc., Germantown, MD, USA) [27].…”

Section: Study (Fukuchi)mentioning

confidence: 99%

“…Marker trajectories and ground reaction force (GRF) were low passed filtered at a matched frequency of 6Hz (4 th Order, zero-lag, Butterworth) [27]. A seven segment lower limb, 6DOF joint model was developed in Visual 3D software (C-motion Inc., Germantown, MD, USA) [27]. A force plate threshold of 50N was used to determine gait events of initial contact and toe-off.…”

Section: Study (Fukuchi)mentioning

confidence: 99%

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The mechanical energetics of walking across the adult lifespan

et al. 2021

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Purpose Understanding what constitutes normal walking mechanics across the adult lifespan is crucial to the identification and intervention of early decline in walking function. Existing research has assumed a simple linear alteration in peak joint powers between young and older adults. The aim of the present study was to quantify the potential (non)linear relationship between age and the joint power waveforms of the lower limb during walking. Methods This was a pooled secondary analysis of the authors’ (MT, KD, JJ) and three publicly available datasets, resulting in a dataset of 278 adults between the ages of 19 to 86 years old. Three-dimensional motion capture with synchronised force plate assessment was performed during self-paced walking. Inverse dynamics were used to quantity joint power of the ankle, knee, and hip, which were time-normalized to 100 stride cycle points. Generalized Additive Models for location, scale and shape (GAMLSS) was used to model the effect of cycle points, age, walking speed, stride length, height, and their interaction on the outcome of each joint’s power. Results At both 1m/s and 1.5 m/s, A2 peaked at the age of 60 years old with a value of 3.09 (95% confidence interval [CI] 2.95 to 3.23) W/kg and 3.05 (95%CI 2.94 to 3.16), respectively. For H1, joint power peaked with a value of 0.40 (95%CI 0.31 to 0.49) W/kg at 1m/s, and with a value of 0.78 (95%CI 0.72 to 0.84) W/kg at 1.5m/s, at the age of 20 years old. For H3, joint power peaked with a value of 0.69 (95%CI 0.62 to 0.76) W/kg at 1m/s, and with a value of 1.38 (95%CI 1.32 to 1.44) W/kg at 1.5m/s, at the age of 70 years old. Conclusions Findings from this study do not support a simple linear relationship between joint power and ageing. A more in-depth understanding of walking mechanics across the lifespan may provide more opportunities to develop early clinical diagnostic and therapeutic strategies for impaired walking function. We anticipate that the present methodology of pooling data across multiple studies, is a novel and useful research method to understand motor development across the lifespan.

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Defining gait patterns using Parallel Factor 2 (PARAFAC2): A new analysis of previously published data

Cited by 5 publications

References 26 publications

Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning

Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning

The mechanical energetics of walking across the adult lifespan

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