Movement Analysis and Biomechanical Simulation for Children with Cerebral Palsy and Equinus gait

Abstract

Cerebral palsy is a well-recognised neurodevelopmental condition beginning in early childhood and persisting throughout the lifespan of the affected individual. Neuromuscular disorders and musculoskeletal deformities result in gait abnormalities for children with cerebral palsy. Equinus gait, characterised by excess plantarflexion, is one of the most common abnormal gait patterns and receives much attention from surgeons and therapists. Three-dimensional gait analysis plays an essential role in cerebral palsy management. However, the conventional optoelectronic three-dimensional gait analysis system is expensive and not user-friendly to individuals without the necessary expertise, hampering its application in clinical practice. In addition, in-depth knowledge regarding ankle muscle activation and force generation ability during ambulation can help clinical practitioners develop suitable treatment strategies for children with cerebral palsy and equinus gait. Therefore, the first aim of this thesis was to determine the validity and reliability of gait parameters assessed by Kinect-based markerless gait analysis systems for children with cerebral palsy. The second aim of this thesis was to determine the effect of involving more personalised musculoskeletal information on ankle muscle force prediction for children with cerebral palsy and equinus gait. The work undertaken in this thesis includes five experimental studies. The first three studies determined the validity, and the reliability, of cost-effective markerless threedimensional gait analysis systems based on Kinect sensors when evaluating crucial gait parameters for children with cerebral palsy. The last two studies investigated the effect of involving pre-calculated joint angles for musculoskeletal model scaling, and applying image-based musculoskeletal models on ankle muscle force prediction, for children with cerebral palsy and equinus gait. The results of the first two studies revealed that the single Kinect V2-based threedimensional gait analysis system was unable to provide accurate joint kinematic estimations. Its validity could be improved by implementing calibration algorithms, including the linear regression-based method and the long short-term memory recurrent neural network-based approach. Also, some hip and knee sagittal kinematic variables (e.g., maximal/minimal hip flexion/extension angles and maximal knee flexion/extension angles) demonstrated good inter-day reliability. Most of the selected gait spatiotemporal variables (e.g., step length/width/time, stride length/time, gait speed) captured by the single Kinect V2-based gait analysis system were accurate and reliable. Furthermore, this system could accurately assess the minimal margin of stability, an essential indicator of fall risk in previous studies. Although the single Kinect V2-based gait analysis system could not provide subtle gait metric evaluations, it still had the potential to be utilised as a portable alternative for some coarse analyses (e.g., gait classification and screening), assess gait stability and monitor gait progressions. In the third study, a dual Azure Kinect gait analysis system was developed based on the point cloud registration algorithm to provide full-scene motion capture. The results demonstrated that the dual Azure Kinect system could still not offer an accurate kinematic assessment. In the fourth study, children with CP and equinus gait were recruited to conduct the three-dimensional gait analysis. Each participant’s ankle muscle forces were estimated by static optimisation and EMG-assist modelling approaches with two scaled musculoskeletal models. The results demonstrated that imposing pre-calculated ankle angles during musculoskeletal model scaling could improve the agreement between ankle dorsi/plantarflexion angles calculated by inverse kinematics and referential direct kinematics. Implementing pre-calculated joint angles during musculoskeletal model scaling could also affect muscle force modelling performance. For the static optimisation approach, the tibialis anterior muscle force prediction was more likely to be influenced. For the EMG-assisted modelling, the gastrocnemius muscle force prediction was more likely to be affected. Scaling with pre-calculated joint angles was recommended due to the better consistency between joint angles estimated by direct kinematics and inverse kinematics, facilitating communication among researchers from different research disciplines. In the fifth study, patient-specific musculoskeletal models were developed for the participants by using MRI scan images of each participant. The results demonstrated that involving personalised joint rotation axes derived from MRIs influenced the inverse kinematic calculation and muscle force prediction, showing the importance of involving personalised anatomical information derived from medical images in neuromusculoskeletal modelling for children with cerebral palsy and equinus gait. In summary, this thesis determined the validity and reliability of two Kinect-based cost-effective gait analysis systems for children with cerebral palsy. The feasibility of utilising Kinect-based three-dimensional gait analysis in screening gait patterns, monitoring gait progressions, and evaluating gait stability is established. This thesis also provided methodological evidence for clinicians and researchers who intend to acquire more personalised biomechanical simulation results for children with cerebral palsy and equinus gait.