A novel approach to quantify dynamic variability during walking

Bibliography: p. 98-114Includes copies of ethics approval. Original copies with original Partial Copyright Licence

A holistic approach to study the temporal variability in gait

Movement variability has become an important field of research and has been studied to gain a better understanding of the neuro-muscular control of human movements. In addition to studies investigating “amplitude variability” there are a growing number of studies assessing the “temporal variability” in movements by applying non-linear analysis techniques. One limitation of the studies available to date is that they quantify variability features in specific, pre-selected biomechanical or physiological variables. In many cases it remains unclear if and to what degree these pre-selected variables quantify characteristics of the whole body movement. This technical note proposes to combine two analysis techniques that have already been applied for gait analysis in order to quantify variability features in walking with variables whose significance for the whole movements are known. Gait patterns were recorded using a full-body marker set on the subjects whose movements were captured with a standard motion tracing system. For each time frame the coordinates of all markers were interpreted as a high-dimensional “posture vector”. A principal component analysis (PCA) conducted on these posture vectors identified the main one-dimensional movement components of walking. Temporal variability of gait was then quantified by calculating the maximum Lyapunov Exponent (LyE) of these main movement components. The effectiveness of this approach was demonstrated by determining differences in temporal variability between walking in unstable shoes and walking in a normal athletic-type control shoe. Several additional conceptual and practical advantages of this combination of analysis methods were discussed

A computational approach to calculate personalized pennation angle based on MRI: effect on motion analysis

Purpose Muscles are the primary component responsible for the locomotion and change of posture of the human body. The physiologic basis of muscle force production and movement is determined by the muscle architecture (maximum muscle force, Fmo, optimal muscle fiber length, lmo, tendon slack length, lts, and pennation angle at optimal muscle fiber length, φ0). The pennation angle is related to the maximum force production and to the range of motion. The aim of this study was to investigate a computational approach to calculate subject-specific pennation angle from magnetic resonance images (MRI)-based 3D anatomical model and to determine the impact of this approach on the motion analysis with personalized musculoskeletal models. Methods A 3D method that calculates the pennation angle using MRI was developed. The fiber orientations were automatically computed, while the muscle line of action was determined using approaches based on anatomical landmarks and on centroids of image segmentation. Three healthy male volunteers were recruited for MRI scanning and motion capture acquisition. This work evaluates the effect of subject-specific pennation angle as musculoskeletal parameter in the lower limb, focusing on the quadriceps group. A comparison was made for assessing the contribution of personalized models on motion analysis. Gait and deep squat were analyzed using neuromuscular simulations (OpenSim). Results The results showed variation of the pennation angle between the generic and subject-specific models, demonstrating important interindividual differences, especially for the vastus intermedius and vastus medialis muscles. The pennation angle variation between personalized and generic musculoskeletal models generated significant variation in muscle moments and forces during dynamic motion analysis. Conclusions A MRI-based approach to define subject-specific pennation angle was proposed and evaluated in motion analysis models. The significant differences obtained for the moments and muscle forces in quadriceps muscles indicate that a personalized approach in modeling the pennation angle can provide more individual details when investigating motion behaviors in specific subjects