Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Characterization and modelling of complex motion patterns

Movement analysis is the principle of any interaction with the world and the survival of living beings completely depends on the effciency of such analysis. Visual systems have remarkably developed eficient mechanisms that analyze motion at different levels, allowing to recognize objects in dynamical and cluttered environments. In artificial vision, there exist a wide spectrum of applications for which the study of complex movements is crucial to recover salient information. Yet each domain may be different in terms of scenarios, complexity and relationships, a common denominator is that all of them require a dynamic understanding that captures the relevant information. Overall, current strategies are highly dependent on the appearance characterization and usually they are restricted to controlled scenarios. This thesis proposes a computational framework that is inspired in known motion perception mechanisms and structured as a set of modules. Each module is in due turn composed of a set of computational strategies that provide qualitative and quantitative descriptions of the dynamic associated to a particular movement. Diverse applications were herein considered and an extensive validation was performed for each of them. Each of the proposed strategies has shown to be reliable at capturing the dynamic patterns of different tasks, identifying, recognizing, tracking and even segmenting objects in sequences of video.Resumen. El análisis del movimiento es el principio de cualquier interacción con el mundo y la supervivencia de los seres vivos depende completamente de la eficiencia de este tipo de análisis. Los sistemas visuales notablemente han desarrollado mecanismos eficientes que analizan el movimiento en diferentes niveles, lo cual permite reconocer objetos en entornos dinámicos y saturados. En visión artificial existe un amplio espectro de aplicaciones para las cuales el estudio de los movimientos complejos es crucial para recuperar información saliente. A pesar de que cada dominio puede ser diferente en términos de los escenarios, la complejidad y las relaciones de los objetos en movimiento, un común denominador es que todos ellos requieren una comprensión dinámica para capturar información relevante. En general, las estrategias actuales son altamente dependientes de la caracterización de la apariencia y por lo general están restringidos a escenarios controlados. Esta tesis propone un marco computacional que se inspira en los mecanismos de percepción de movimiento conocidas y esta estructurado como un conjunto de módulos. Cada módulo esta a su vez compuesto por un conjunto de estrategias computacionales que proporcionan descripciones cualitativas y cuantitativas de la dinámica asociada a un movimiento particular. Diversas aplicaciones fueron consideradas en este trabajo y una extensa validación se llevó a cabo para cada uno de ellas. Cada una de las estrategias propuestas ha demostrado ser fiable en la captura de los patrones dinámicos de diferentes tareas identificando, reconociendo, siguiendo e incluso segmentando objetos en secuencias de video.Doctorad

Negotiating International Regimes: Lessons Learned from the United Nations Conference on Environment and Development (UNCED)

The UN Conference on Environment and Development (UNCED) produced above all the beginning of a new process, rather than arriving at a singular and conclusive agreement to a specific problem - often the more common outcome of negotiation. Whereas outwardly the UNCED process was familiar, it soon was to distinguish itself as a singularly instructive set of "regime-building" negotiations. This important new work, developed by IIASA, explains and analyses the negotiation process of building international environmental regimes. Its value will be considerable as the international community faces the need to establish the variety of sub-regimes (desertification, forestry, and others) spawned by UNCED. This work offers the conceptual and practical building blocks, as learned from UNCED, to all those engaged, and interested in the means to ensure sustainable development and the economic and environmental well-being of humanity. The text is accompanied by valuable appendices

Spatial and temporal background modelling of non-stationary visual scenes

PhDThe prevalence of electronic imaging systems in everyday life has become increasingly apparent in recent years. Applications are to be found in medical scanning, automated manufacture, and perhaps most significantly, surveillance. Metropolitan areas, shopping malls, and road traffic management all employ and benefit from an unprecedented quantity of video cameras for monitoring purposes. But the high cost and limited effectiveness of employing humans as the final link in the monitoring chain has driven scientists to seek solutions based on machine vision techniques. Whilst the field of machine vision has enjoyed consistent rapid development in the last 20 years, some of the most fundamental issues still remain to be solved in a satisfactory manner. Central to a great many vision applications is the concept of segmentation, and in particular, most practical systems perform background subtraction as one of the first stages of video processing. This involves separation of ‘interesting foreground’ from the less informative but persistent background. But the definition of what is ‘interesting’ is somewhat subjective, and liable to be application specific. Furthermore, the background may be interpreted as including the visual appearance of normal activity of any agents present in the scene, human or otherwise. Thus a background model might be called upon to absorb lighting changes, moving trees and foliage, or normal traffic flow and pedestrian activity, in order to effect what might be termed in ‘biologically-inspired’ vision as pre-attentive selection. This challenge is one of the Holy Grails of the computer vision field, and consequently the subject has received considerable attention. This thesis sets out to address some of the limitations of contemporary methods of background segmentation by investigating methods of inducing local mutual support amongst pixels in three starkly contrasting paradigms: (1) locality in the spatial domain, (2) locality in the shortterm time domain, and (3) locality in the domain of cyclic repetition frequency. Conventional per pixel models, such as those based on Gaussian Mixture Models, offer no spatial support between adjacent pixels at all. At the other extreme, eigenspace models impose a structure in which every image pixel bears the same relation to every other pixel. But Markov Random Fields permit definition of arbitrary local cliques by construction of a suitable graph, and 3 are used here to facilitate a novel structure capable of exploiting probabilistic local cooccurrence of adjacent Local Binary Patterns. The result is a method exhibiting strong sensitivity to multiple learned local pattern hypotheses, whilst relying solely on monochrome image data. Many background models enforce temporal consistency constraints on a pixel in attempt to confirm background membership before being accepted as part of the model, and typically some control over this process is exercised by a learning rate parameter. But in busy scenes, a true background pixel may be visible for a relatively small fraction of the time and in a temporally fragmented fashion, thus hindering such background acquisition. However, support in terms of temporal locality may still be achieved by using Combinatorial Optimization to derive shortterm background estimates which induce a similar consistency, but are considerably more robust to disturbance. A novel technique is presented here in which the short-term estimates act as ‘pre-filtered’ data from which a far more compact eigen-background may be constructed. Many scenes entail elements exhibiting repetitive periodic behaviour. Some road junctions employing traffic signals are among these, yet little is to be found amongst the literature regarding the explicit modelling of such periodic processes in a scene. Previous work focussing on gait recognition has demonstrated approaches based on recurrence of self-similarity by which local periodicity may be identified. The present work harnesses and extends this method in order to characterize scenes displaying multiple distinct periodicities by building a spatio-temporal model. The model may then be used to highlight abnormality in scene activity. Furthermore, a Phase Locked Loop technique with a novel phase detector is detailed, enabling such a model to maintain correct synchronization with scene activity in spite of noise and drift of periodicity. This thesis contends that these three approaches are all manifestations of the same broad underlying concept: local support in each of the space, time and frequency domains, and furthermore, that the support can be harnessed practically, as will be demonstrated experimentally

Hip & Spine Mechanics - Understanding the linkage from several perspectives of injury mechanisms to rehabilitation using biomechanical modelling

One recent megatrend in medicine is that of “precision medicine” whereby a precise diagnosis leads to a precise intervention for superior results. This thesis was undertaken to enhance the understanding of spine and hip interactions and facilitate precision in both detection and the intervention of mechanical and neurological based disorders. The hip and spine are highly integrated structures. In order to adequately examine and improve the understanding of the complex mechanical linkage between the two, development of a highly biofidelic Hip-Spine Model (HSM) was pursued. Given that no model existed that incorporated the necessary detail, several challenges regarding model development needed to be addressed. It was clear that biofidelity of the model depended on a better understanding and representation of the passive hip stiffness in both males and females. Thus, the experimental data of passive stiffness was evaluated in conjunction with the HSM passive stiffness model predictions. Next, known Anterior Cruciate Ligament (ACL) injury risk factors, such as dynamic knee valgus (DKV) were examined in a female population where both the kinetic and kinematic variables of the hip and spine were evaluated to assist in differentiating those deemed at-risk and not-at-risk during the drop vertical jump (DVJ) procedure. Finally, an atlas of rehabilitation exercises was constructed to guide program design and progression/regressions of rehabilitation protocols for those with back and hip concerns. Each of these themes were unified around the overall goal of this thesis, that being the understanding of the hip-spine mechanical linkage pertaining to injury mechanisms and a guide for rehabilitation of hip-spine disorders. The first task was the development of the HSM. This model is anatomically detailed and driven by biological signals obtained from the individual to provide new insights into the understanding of the linkage in a way that was sensitive to the unique movement strategies of the individual. The HSM is an expansion of the previously established ‘Spine Model’ (SM), developed by Stuart McGill and his team over the past 37 years. The model anatomy was expanded from the current SM using the most complete single subject lower limb data set available know as the Twente Lower Extremity Model (TLEM). Hip ligaments were also added to enhance the passive behaviour of the model. An electromyographic (EMG) driven approach with subject specific kinematics was used to compute the model outputs that consisted of tissue and joint loads. Thus, the first objective of this thesis was to examine the interactions of hip and spine mechanics using a newly developed Hip-Spine Model (HSM) and then to investigate a spectrum of injury mechanisms and rehabilitation exercises. The next objective was to evaluate passive hip stiffness to enhance the biofidelity of the model and the understanding of hip mechanics in both males and females. A novel testing apparatus was designed and fabricated for measuring hip stiffness which could easily be adapted to clinical settings. This study also serves to establish normative baselines for passive hip stiffness in vivo. The third objective of this thesis was to examine issues of normal function and potential injury mechanisms. For example, ACL injury risk has been linked with some knee kinematic and kinetic patterns however, hip and spine interactions have not been appropriately explored nor have neuromuscular control strategies. This thesis linked the mechanical variables which differentiate at-risk landings of the DVJ task versus non-at-risk landing in females to enhance the understanding of risk behaviours. Clear differences in hip and spine control were documented to differentiate high and low valgus landings. Adding this knowledge to the current understanding of ACL injury risk will lead to the development of superior and more specific coaching cues to decrease tissue stress/strain concentrations. This approach will underpin intervention strategies leading to lower risk behaviour and correspondingly lower injury risk among female athletes. The final objective of this thesis was to evaluate the appropriateness of rehabilitation exercises to address hip spine disorders. Currently, there exists a myriad of exercises but little evidence to guide clinical reasoning or decisions for exercise choice, progression, volume and technique. Currently missing from the literature is knowledge of tissue and joint loads in combination with muscle activation patterns. This knowledge will facilitate better matching of specific exercises for specific disorders. The main objectives of this thesis were: (1) The development of the anatomically detailed, biologically driven HSM which successfully computes joint and tissue loads unique to the individual and their neuromuscular control strategy. (2) The establishment of passive hip stiffness for males and females. (3) To enhance understanding of both neurological and tissue loading characteristics associated with ACL injury risk which when added to the current knowledge provides the opportunity for more precise interventions. (4) The beginning of the development of an atlas for rehabilitation exercises to guide prescriptions that can be matched to specific hip-spine disorders. These findings have the potential to enhance precision medicine in the area of musculoskeletal health – where precision medicine is currently lacking

Investigation and Quantification of FES Exercise – Isometric Electromechanics and Perceptions of Its Usage as an Exercise Modality for Various Populations

Functional Electrical Stimulation (FES) is the triggering of muscle contraction by use of an electrical current. It can be used to give paralyzed individuals several health benefits, through allowing artificial movement and exercise. Although many FES devices exist, many aspects require innovation to increase usability and home translation. In addition, the effect of changing electrical parameters on limb biomechanics is not entirely understood; in particular with regards to stimulation duty cycle. This thesis has two distinct components. In the first (public health component), interview studies were conducted to understand several issues related to FES technology enhancement, implementation and home translation. In the second (computational biomechanics component), novel signal processing algorithms were designed that can be used to measure mechanical responses of muscles subjected to electrical stimulation. These experiments were performed by changing duty cycle and measuring its effect on quadriceps-generated knee torque. The studies of this thesis have presented several ideas, toolkits and results which have the potential to guide future FES biomechanics studies and the translatability of systems into regular usage for patients. The public health studies have provided conceptual frameworks upon which FES may be used in the home by patients. In addition, they have elucidated a range of issues that need to be addressed should FES technology reach its true potential as a therapy. The computational biomechanics studies have put forward novel data analysis techniques which may be used for understanding how muscle responds to electrical stimulation, as measured via torque. Furthermore, the effect of changing the electrical stimulation duty cycle on torque was successfully described, adding to an understanding of how electrical stimulation parameter modulation can influence joint biomechanics

WATCHING PEOPLE: ALGORITHMS TO STUDY HUMAN MOTION AND ACTIVITIES

Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into three major areas related to the interpretation of human motion: tracking of human motion using virtual pan-tilt-zoom (vPTZ) camera, recognition of human motions and human behaviors segmentation. In the field of human motion tracking, a virtual environment for PTZ cameras (vPTZ) is presented to overcame the mechanical limitations of PTZ cameras. The vPTZ is built on equirectangular images acquired by 360° cameras and it allows not only the development of pedestrian tracking algorithms but also the comparison of their performances. On the basis of this virtual environment, three novel pedestrian tracking algorithms for 360° cameras were developed, two of which adopt a tracking-by-detection approach while the last adopts a Bayesian approach. The action recognition problem is addressed by an algorithm that represents actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. The proposed method learns a codebook of frequent sequential patterns by means of an apriori-like algorithm. An action is then represented with a Bag-of-Frequent-Sequential-Patterns approach. In the last part of this thesis a methodology to semi-automatically annotate behavioral data given a small set of manually annotated data is presented. The resulting methodology is not only effective in the semi-automated annotation task but can also be used in presence of abnormal behaviors, as demonstrated empirically by testing the system on data collected from children affected by neuro-developmental disorders

Artifacts, Others, and Temporality: An Enactive and Phenomenological Approach to Material Agency

Artifacts, Others, and Temporality: An Enactive and Phenomenological Approach to Material Agenc