Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

DETERMINING SELECTIVE VOLUNTARY MOTOR CONTROL OF THE LOWER EXTREMITY IN CHILDREN WITH CEREBRAL PALSY

For physiotherapists working in neuro-paediatric gait-rehabilitation, improving motor control of the lower extremity is a major focus. Nevertheless, our understanding of selective voluntary motor control (SVMC) is in its infancy. This PhD project aimed to contribute to close this gap by investigating the nature of SVMC of the lower extremity in children with cerebral palsy (CP) and providing a psychometric robust yet sensitive measurement instrument for quantifying SVMC. The first study investigated the influence of SVMC and other lower extremity and trunk motor impairments on gait capacity using multiple regression-analyses. Although SVMC was not kept within the final model, these study results revealed the importance of SVMC in relation to muscle strength, trunk control and gait capacity. The aim of the second study was to establish validity and reliability of the German version of the ‘Selective Control Assessment of the Lower Extremity’ (SCALE). Although the psychometric properties of the German SCALE were good, information about its responsiveness is lacking. Accordingly, a systematic review was carried out to identify a SVMC measurement instrument with the highest level of evidence for its psychometric properties and best clinical utility. As the findings showed the absence of appropriate, responsive SVMC measures, the aim of the last study was to modify the existing SCALE to make it more sensitive. Due to the positive findings in relation to the psychometric properties of the SCALE, its procedure was combined with a surface electromyography Similarity Index (SI). The first validity and reliability results of the SCALE-SI are promising and serve as benchmarks when applying the SCALE-SI in future clinical and scientific practice. However, to use the SCALE-SI as an outcome measure for detecting therapy-induced changes of SVMC in children with CP, its responsiveness needs to be evaluated in future studies. Key Words: cerebral palsy, selective voluntary motor control, psychometric properties, lower extremity, gait rehabilitatio

DETERMINING SELECTIVE VOLUNTARY MOTOR CONTROL OF THE LOWER EXTREMITY IN CHILDREN WITH CEREBRAL PALSY

For physiotherapists working in neuro-paediatric gait-rehabilitation, improving motor control of the lower extremity is a major focus. Nevertheless, our understanding of selective voluntary motor control (SVMC) is in its infancy. This PhD project aimed to contribute to close this gap by investigating the nature of SVMC of the lower extremity in children with cerebral palsy (CP) and providing a psychometric robust yet sensitive measurement instrument for quantifying SVMC. The first study investigated the influence of SVMC and other lower extremity and trunk motor impairments on gait capacity using multiple regression-analyses. Although SVMC was not kept within the final model, these study results revealed the importance of SVMC in relation to muscle strength, trunk control and gait capacity. The aim of the second study was to establish validity and reliability of the German version of the ‘Selective Control Assessment of the Lower Extremity’ (SCALE). Although the psychometric properties of the German SCALE were good, information about its responsiveness is lacking. Accordingly, a systematic review was carried out to identify a SVMC measurement instrument with the highest level of evidence for its psychometric properties and best clinical utility. As the findings showed the absence of appropriate, responsive SVMC measures, the aim of the last study was to modify the existing SCALE to make it more sensitive. Due to the positive findings in relation to the psychometric properties of the SCALE, its procedure was combined with a surface electromyography Similarity Index (SI). The first validity and reliability results of the SCALE-SI are promising and serve as benchmarks when applying the SCALE-SI in future clinical and scientific practice. However, to use the SCALE-SI as an outcome measure for detecting therapy-induced changes of SVMC in children with CP, its responsiveness needs to be evaluated in future studies. Key Words: cerebral palsy, selective voluntary motor control, psychometric properties, lower extremity, gait rehabilitatio

Deep learning for gait prediction: an application to exoskeletons for children with neurological disorders

Cerebral Palsy, a non-progressive neurological disorder, is a lifelong condition. While it has no cure, clinical intervention aims to minimise the impact of the disability on individuals' lives. Wearable robotic devices, like exoskeletons, have been rapidly advancing and proving to be effective in rehabilitating individuals with gait pathologies. The utilization of artificial intelligence (AI) algorithms in controlling exoskeletons, particularly at the supervisory level, has emerged as a valuable approach. These algorithms rely on input from onboard sensors to predict gait phase, user intention, or joint kinematics. Using AI to improve the control of robotic devices not only enhances human-robot interaction but also has the potential to improve user comfort and functional outcomes of rehabilitation, and reduce accidents and injuries. In this research study, a comprehensive systematic literature review is conducted, exploring the various applications of AI in lower-limb robotic control. This review focuses on methodological parameters such as sensor usage, training demographics, sample size, and types of models while identifying gaps in the existing literature. Building on the findings of the review, subsequent research leveraged the power of deep learning to predict gait trajectories for the application of rehabilitative exoskeleton control. This study addresses a gap in the existing literature by focusing on predicting pathological gait trajectories, which exhibit higher inter- and intra-subject variability compared to the gait of healthy individuals. The research focused on the gait of children with neurological disorders, particularly Cerebral Palsy, as they stand to benefit greatly from rehabilitative exoskeletons. State-of-the-art deep learning algorithms, including transformers, fully connected neural networks, convolutional neural networks, and long short-term memory networks, were implemented for gait trajectory prediction. This research presents findings on the performance of these models for short-term and long-term recursive predictions, the impact of varying input and output window sizes on prediction errors, the effect of adding variable levels of Gaussian noise, and the robustness of the models in predicting gait at speeds within and outside the speed range of the training set. Moreover, the research outlines a methodology for optimising the stability of long-term forecasts and provides a comparative analysis of gait trajectory forecasting for typically developing children and children with Cerebral Palsy. A novel approach to generating adaptive trajectories for children with Cerebral Palsy, which can serve as reference trajectories for position-controlled exoskeletons, is also presented

Muscle synergy analysis of lower-limb movements

Dissertação de mestrado integrado em Biomedical Engineering (área de especialização em Medical Electronics)Neurological disorders and trauma often lead to impaired lower-limb motor coordination. Understanding how muscles combine to produce movement can directly benefit assistive solutions to those afflicted with these impairments. A theory in neuromusculoskeletal research, known as muscle synergies, has shown promising results in applications for this field. This hypothesis postulates that the Central Nervous System controls motor tasks through the time-variant combinations of modules (or synergies), each representing the co-activation of a group of muscles. There is, however, no unifying, evidence-based framework to ascertain muscle synergies, as synergy extraction methods vary greatly in the literature. Publications also focus on gait analysis, leaving a knowledge gap when concerning motor tasks important to daily life such as sitting and standing. The purpose of this dissertation is the development of a robust, evidence-based, task-generic synergy extraction framework unifying the divergent methodologies of this field of study, and to use this framework to study healthy muscle synergies on several activities of daily living: walking, sit-to-stand, stand-to-sit and knee flexion and extension. This was achieved by designing and implementing a cross-validated Non-Negative Matrix Factorization process and applying it to muscle electrical activity data. A preliminary study was undertaken to tune this configuration regarding cross-validating proportions, data structuring prior to factorization and evaluating criteria quantifying accuracy in modularity findings. Muscle synergies results were then investigated for different performing speeds to determine if their structure differed, and for consistency across subjects, to ascertain if a common set of muscle synergies underlay control on all subjects equally. Results revealed that the implemented framework was consistent in its ability to capture modularity (p < 0:05). The movements’ synergies also did not differ across the studied range of speeds (except one module in Knee Flexion) (p < 0:05). Additionally, a common set of muscle synergies was present across several subjects (p < 0:05), but shared commonality across every participant was only observed for the walking trials, for which much larger amounts of data were collected. Overall, the established framework is versatile and applicable for different lower-limb movements; muscle synergies findings for the examined movements may also be used as control references in assistive devices.As perturbações e traumas neurológicos afetam frequentemente a coordenação motora dos membros inferiores. Uma teoria recente em investigação neuromusculo-esquelética, denominada de sinergias musculares, tem demonstrado resultados promissores em soluções de assistência à população afetada por estes distúrbios. Esta teoria propõe que o Sistema Nervoso Central controla as tarefas motoras através de combinações variantes no tempo de módulos (ou sinergias), sendo que cada um representa a co-ativação de um grupo de músculos. No entanto, não existe nenhum processo uniformizante, empiricamente justificado para determinar sinergias musculares, porque os métodos de extração de sinergias variam muito na literatura. Para além disso, as publicações normalmente focam-se em análise da marcha, deixando uma lacuna de conhecimento em tarefas motoras do dia-a-dia, tais como sentar e levantar. O objetivo desta dissertação é o desenvolvimento de um processo robusto, genérico e empiricamente justificado de extração de sinergias em várias tarefas motoras, unindo as metodologias divergentes neste campo de estudo, e subsequentemente utilizar este processo para estudar sinergias musculares de sujeitos saudáveis em várias atividades do dia-a-dia: marcha, erguer-se de pé partir de uma posição sentada, sentar-se a partir de uma posição de pé e extensão e flexão do joelho. Isto foi alcançado através da implementação de um processo de cross-validated Non-Negative Matrix Factorization e subsequente aplicação em dados de atividade elétrica muscular. Um estudo preliminar foi realizado para configurar este processo relativamente às proporções de cross-validation, estruturação de dados antes da fatorização e seleção de critério que quantifique o sucesso da representação modular dos dados. Os resultados da extração de sinergias de diferentes velocidades de execução foram depois examinados no sentido de descobrir se este fator influenciava a estrutura dos módulos motores, assim como se semelhanças entre as sinergias de diferentes sujeitos apontavam para um conjunto comum de sinergias musculares subjacente ao controlo do movimento. Os resultados revelaram que o processo implementado foi consistente na sua capacidade de capturar a modularidade nos dados recolhidos (p < 0:05). As sinergias de todos os movimentos também não diferiram para toda a gama de velocidades estudada (exceto um módulo na flexão do joelho) (p < 0:05). Por fim, um conjunto comum de sinergias musculares esteve presente em vários sujeitos (p < 0:05), mas só esteve presente em todos os sujeitos de igual forma para a marcha, para a qual a quantidade de dados recolhida foi muito maior. Globalmente, o processo implementado é versátil e aplicável a diferentes movimentos dos membros inferiores; os resultados das sinergias musculares para os movimentos examinados podem também ser utilizado como referências de controlo para dispositivos de assistência

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%