Design and Evaluation of Pediatric Gait Rehabilitation Robots

Gait therapy methodologies were studied and analyzed for their potential for pediatric patients. Using data from heel, metatarsal, and toe trajectories, a nominal gait trajectory was determined using Fourier transforms for each foot point. These average trajectories were used as a basis of evaluating each gait therapy mechanism. An existing gait therapy device (called ICARE) previously designed by researchers, including engineers at the University of Nebraska-Lincoln, was redesigned to accommodate pediatric patients. Unlike many existing designs, the pediatric ICARE did not over- or under-constrain the patient’s leg, allowing for repeated, comfortable, easily-adjusted gait motions. This design was assessed under clinical testing and deemed to be acceptable. A gait rehabilitation device was designed to interface with both pediatric and adult patients and more closely replicate the gait-like metatarsal trajectory compared to an elliptical machine. To accomplish this task, the nominal gait path was adjusted to accommodate for rotation about the toe, which generated a new trajectory that was tangent to itself at the midpoint of the stride. Using knowledge of the bio-mechanics of the foot, the gait path was analyzed for its applicability to the general population. Several trajectory-replication methods were evaluated, and the crank-slider mechanism was chosen for its superior performance and ability to mimic the gait path adequately. Adjustments were made to the gait path to further optimize its realization through the crank-slider mechanism. Two prototypes were constructed according to the slider-crank mechanism to replicate the gait path identified. The first prototype, while more accurately tracing the gait path, showed difficulty in power transmission and excessive cam forces. This prototype was ultimately rejected. The second prototype was significantly more robust. However, it lacked several key aspects of the original design that were important to matching the design goals. Ultimately, the second prototype was recommended for further work in gait-replication research. Advisor: Carl A. Nelso

SEGMENTATION, RECOGNITION, AND ALIGNMENT OF COLLABORATIVE GROUP MOTION

Modeling and recognition of human motion in videos has broad applications in behavioral biometrics, content-based visual data analysis, security and surveillance, as well as designing interactive environments. Significant progress has been made in the past two decades by way of new models, methods, and implementations. In this dissertation, we focus our attention on a relatively less investigated sub-area called collaborative group motion analysis. Collaborative group motions are those that typically involve multiple objects, wherein the motion patterns of individual objects may vary significantly in both space and time, but the collective motion pattern of the ensemble allows characterization in terms of geometry and statistics. Therefore, the motions or activities of an individual object constitute local information. A framework to synthesize all local information into a holistic view, and to explicitly characterize interactions among objects, involves large scale global reasoning, and is of significant complexity. In this dissertation, we first review relevant previous contributions on human motion/activity modeling and recognition, and then propose several approaches to answer a sequence of traditional vision questions including 1) which of the motion elements among all are the ones relevant to a group motion pattern of interest (Segmentation); 2) what is the underlying motion pattern (Recognition); and 3) how two motion ensembles are similar and how we can 'optimally' transform one to match the other (Alignment). Our primary practical scenario is American football play, where the corresponding problems are 1) who are offensive players; 2) what are the offensive strategy they are using; and 3) whether two plays are using the same strategy and how we can remove the spatio-temporal misalignment between them due to internal or external factors. The proposed approaches discard traditional modeling paradigm but explore either concise descriptors, hierarchies, stochastic mechanism, or compact generative model to achieve both effectiveness and efficiency. In particular, the intrinsic geometry of the spaces of the involved features/descriptors/quantities is exploited and statistical tools are established on these nonlinear manifolds. These initial attempts have identified new challenging problems in complex motion analysis, as well as in more general tasks in video dynamics. The insights gained from nonlinear geometric modeling and analysis in this dissertation may hopefully be useful toward a broader class of computer vision applications

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Proceedings XXIII Congresso SIAMOC 2023

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione

Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

Exploring the effects of robotic design on learning and neural control

The ongoing deep learning revolution has allowed computers to outclass humans in various games and perceive features imperceptible to humans during classification tasks. Current machine learning techniques have clearly distinguished themselves in specialized tasks. However, we have yet to see robots capable of performing multiple tasks at an expert level. Most work in this field is focused on the development of more sophisticated learning algorithms for a robot's controller given a largely static and presupposed robotic design. By focusing on the development of robotic bodies, rather than neural controllers, I have discovered that robots can be designed such that they overcome many of the current pitfalls encountered by neural controllers in multitask settings. Through this discovery, I also present novel metrics to explicitly measure the learning ability of a robotic design and its resistance to common problems such as catastrophic interference. Traditionally, the physical robot design requires human engineers to plan every aspect of the system, which is expensive and often relies on human intuition. In contrast, within the field of evolutionary robotics, evolutionary algorithms are used to automatically create optimized designs, however, such designs are often still limited in their ability to perform in a multitask setting. The metrics created and presented here give a novel path to automated design that allow evolved robots to synergize with their controller to improve the computational efficiency of their learning while overcoming catastrophic interference. Overall, this dissertation intimates the ability to automatically design robots that are more general purpose than current robots and that can perform various tasks while requiring less computation.Comment: arXiv admin note: text overlap with arXiv:2008.0639

Biomechanical adaptations in the acceleration phase of bend sprinting

During bend sprinting athletes are unable to reach the same maximal speeds achieved on the straight. Since over half of the race distance is run on a curved portion of track, this has implications for race performance. This thesis aims to understand the biomechanical aspects of technique and performance in the acceleration phase of bend sprinting. Chapter 3: The accuracy of two reduced marker sets (lower limb and lower limb and trunk) were evaluated in comparison to a full-body marker set in the calculation of the centre of mass location and associated variables (velocity, touchdown distance and turn of centre of mass). Intraclass correlation coefficients demonstrated excellent agreement between all marker sets. However, the lower limb and trunk model was favoured and recommended for future use due to the marginally better agreement observed and the requirement for only four additional markers compared with the lower limb model. Chapter 4: The within- and between-day reliability of a lower limb and trunk marker set was established during bend sprinting. The results demonstrated that, where possible, data for each participant should be collected in the same session to increase reliability and reduce the required minimum detectable difference. The minimum detectable difference was used in subsequent studies to aid in the interpretation of results. Chapter 5: This marker set was used to identify the biomechanical adaptations of the lower limb during the acceleration phase of bend sprinting with a 36.5 m radius (lane one). Whilst the left limb demonstrated a greater peak hip adduction, peak hip internal rotation and peak ankle eversion on the bend compared with the straight, the right limb was characterised by an increase in peak hip abduction. In addition, with regard to spatio-temporal variables, there was a reduction in left step frequency and touchdown distance. Chapter 6: The use of statistical parametric mapping revealed a lower propulsive force at 38 - 44 % of stance on the bend than straight. In the left limb, this coincided with the use of the oblique axis for push-off at the metatarsophalangeal joint. In addition, mediolateral force was higher on the bend than the straight for the majority of the stance phase (3-96%). Chapter 7: No changes in extensor moment were observed at the hip and knee joints. Large effect sizes suggest a trend towards an increase in left step peak ankle plantar flexor moment. This increased plantar flexor moment may be due to a greater need for stabilisation as a consequence of non-sagittal plane adaptations of the lower limb