A Review of Computational Musculoskeletal Analysis of Human Lower Extremities

Alamdari, Aliakbar; Krovi, Venkat

doi:10.1016/b978-0-12-803137-7.00003-3

Cited by 34 publications

(29 citation statements)

References 107 publications

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“…Sci. 2021, 11,268 prescribed by Park et al Nevertheless, the accent in this work is on analyzing ste walking, and as such, gait onset and gait initiation do not render useful info Moreover, the exact moment of gait onset will not alter whatsoever on the asses steady state walking. A set of conclusive test results is presented as follows.…”

Section: Resultsmentioning

confidence: 97%

“…The average duration of one gait cycle ranges from 0.98 to 1.07 s. The stance phase is the period that the foot is on the ground and accounts for approximately 60-62%, i.e., 0.59 to 0.67 s of the gait cycle, during which the foot is weightbearing as the leg takes the bodyweight and provides a single leg support. The remaining 38-40%, i.e., 0.38 to 0.42 s of one gait cycle is spent in the swing phase, which is the period of time that the foot is off the ground, moving forward and the limb advances [10,11].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics

2020

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Walking is the most basic form of human activity for achieving mobility. As an essential function of the human body, the examination of walking is directed towards the assessment of body mechanics in posture and during movement. This work proposes a wearable smart system for the monitoring and objective evaluation of foot biomechanics during gait. The proposed solution assumes the cross-correlation of the plantar pressure with lower-limb muscular activity, throughout the stance phase of walking. Plantar pressure is acquired with an array of resistive pressure sensors deployed onto a shoe insole along the center of gravity progression line. Lower-limb muscular activity is determined from the electromyogram of the tibialis anterior and gastrocnemius lower limb muscles respectively. Under this scenario, physiological gait assumes the interdependency of plantar pressure on the heel area with activation of the tibialis anterior, as well as plantar pressure on the metatarsal arch/toe area with activation of the gastrocnemius. As such, assessment of gait physiology is performed by comparison of a gait map, formulated based on the footprint–lower-limb muscle cross-correlation results, to a reference gait template. A laboratory proof of concept validates the proposed solution in a test scenario which assumes a normal walking and two pathological walking patterns.

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics

2020

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“…The stride duration parameter estimates the time length of a single gait cycle marked by two successive heel-strike actions. [ 32 , 33 ]…”

Section: Methodsmentioning

confidence: 99%

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

Fraiwan

Hassanin

2021

PLoS ONE

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This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs.

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“…force, momentum) and muscle activation (e.g. force, onset -offset) occur with alterations during the gait cycle that can be extracted for sub-phase analysis [42].…”

Section: Understanding Gait: Clinical-based Outcomes and Conceptual Modelsmentioning

confidence: 99%

Gait analysis in neurological populations: Progression in the use of wearables

Çelik

Stuart

Woo

et al. 2021

Medical Engineering & Physics

107

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A Review of Computational Musculoskeletal Analysis of Human Lower Extremities

Cited by 34 publications

References 107 publications

A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics

A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

Gait analysis in neurological populations: Progression in the use of wearables

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