Evolved Topology Generalized Multi-layer Perceptron (GMLP) for Anatomical Joint Constraint Modelling

The accurate simulation of anatomical joint models is becoming increasingly important for both medical diagnosis and realistic animation applications. Quaternion algebra has been increasingly applied to model rotations providing a compact representation while avoiding singularities. We propose the use of Artificial Neural Networks to accurately simulate joint constraints based on recorded data. This paper describes the application of Genetic Algorithm approaches to neural network training in order to model corrective piece-wise linear / discontinuous functions required to maintain valid joint configurations. The results show that artificial Neural Networks are capable of modeling constraints on the rotation of and around a virtual limb

Support Vector Machines for Anatomical Joint Constraint Modelling

The accurate simulation of anatomical joint models is becoming increasingly important for both realistic animation and diagnostic medical applications. Recent models have exploited unit quaternions to eliminate singularities when modeling orientations between limbs at a joint. This has led to the development of quaternion based joint constraint validation and correction methods. In this paper a novel method for implicitly modeling unit quaternion joint constraints using Support Vector Machines (SVMs) is proposed which attempts to address the limitations of current constraint validation approaches. Initial results show that the resulting SVMs are capable of modeling regular spherical constraints on the rotation of the limb

Self Organising Maps for Anatomical Joint Constraint

The accurate simulation of anatomical joint models is becoming increasingly important for both realistic animation and diagnostic medical applications. Recent models have exploited unit quaternions to eliminate ingularities when modelling orientations between limbs at a joint. This has led to the development of quaternion based joint constraint validation and correction methods. In this paper a novel method for implicitly modelling unit quaternion joint constraints using Self Organizing Maps (SOMs) is proposed which attempts to address the limitations of current constraint validation and correction approaches. Initial results show that the resulting SOMs are capable of modelling regular spherical constraints on the orientation of the limb

Joint Constraint Modelling Using Evolved Topology Generalized Multi-Layer Perceptron(GMLP)

The accurate simulation of anatomical joint models is important for both medical diagnosis and realistic animation applications.  Quaternion algebra has been increasingly applied to model rotations providing a compact representation while avoiding singularities.  This paper describes the application of artificial neural networks topologically evolved using genetic algorithms to model joint constraints directly in quaternion space.  These networks are trained (using resilient back propagation) to model discontinuous vector fields that act as corrective functions ensuring invalid joint configurations are accurately corrected.  The results show that complex quaternion-based joint constraints can be learned without resorting to reduced coordinate models or iterative techniques used in other quaternion based joint constraint approaches

Using biomechanical constraints to improve video-based motion capture

In motion capture applications whose aim is to recover human body postures from various input, the high dimensionality of the problem makes it desirable to reduce the size of the search-space by eliminating a priori impossible configurations. This can be carried out by constraining the posture recovery process in various ways. Most recent work in this area has focused on applying camera viewpoint-related constraints to eliminate erroneous solutions. When camera calibration parameters are available, they provide an extremely efficient tool for disambiguating not only posture estimation, but also 3D reconstruction and data segmentation. Increased robustness is indeed to be gained from enforcing such constraints, which we prove in the context of an optical motion capture framework. Our contribution in this respect resides in having applied such constraints consistently to each main step involved in a motion capture process, namely marker reconstruction and segmentation, followed by posture recovery. These steps are made inter-dependent, where each one constrains the other. A more application-independent approach is to encode constraints directly within the human body model, such as limits on the rotational joints. This being an almost unexplored research subject, our efforts were mainly directed at determining a new method for measuring, representing and applying such joint limits. To the present day, the few existing range of motion boundary representations present severe drawbacks that call for an alternative formulation. The joint limits paradigm we propose not only overcomes these drawbacks, but also allows to capture intra- and inter-joint rotation dependencies, these being essential to realistic joint motion representation. The range of motion boundary is defined by an implicit surface, its analytical expression enabling us to readily establish whether a given joint rotation is valid or not. Furthermore, its continuous and differentiable nature provides us with a means of elegantly incorporating such a constraint within an optimisation process for posture recovery. Applying constrained optimisation to our body model and stereo data extracted from video sequence, we demonstrate the clearly resulting decrease in posture estimation errors. As a bonus, we have integrated our joint limits representation in character animation packages to show how motion can be naturally constrained in this manner

Human Motion Analysis with Wearable Inertial Sensors

High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system

Spherical frame projections for visualising joint range of motion, and a complementary method to capture mobility data

Quantifying joint range of motion (RoM), the reachable poses at a joint, has many applications in research and clinical care. Joint RoM measurements can be used to investigate the link between form and function in extant and extinct animals, to diagnose musculoskeletal disorders and injuries or monitor rehabilitation progress. However, it is difficult to visually demonstrate how the rotations of the joint axes interact to produce joint positions. Here, we introduce the spherical frame projection (SFP), which is a novel 3D visualisation technique, paired with a complementary data collection approach. SFP visualisations are intuitive to interpret in relation to the joint anatomy because they ‘trace’ the motion of the coordinate system of the distal bone at a joint relative to the proximal bone. Furthermore, SFP visualisations incorporate the interactions of degrees of freedom, which is imperative to capture the full joint RoM. For the collection of such joint RoM data, we designed a rig using conventional motion capture systems, including live audio-visual feedback on torques and sampled poses. Thus, we propose that our visualisation and data collection approach can be adapted for wide use in the study of joint function

Quantifying the ergonomic risk and biomechanical exposure in automotive assembly lines

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasAs Lesões Músculo-esqueléticas Relacionadas com o Trabalho (LMERTs) representam 15% do número total de anos de vida perdidos por danos físicos ou doenças com a sua génese no trabalho. De entre os fatores de risco para as LMERTs, no presente estudo, destacam-se as posturas corporais relacionadas com o trabalho. A exposição biomecânica a posturas consideradas prejudiciais tem um impacto negativo na saúde dos trabalhadores, na economia das empresas e na sociedade. A fim de aperceber a prática recorrente de posturas prejudiciais no local de trabalho, têm sido invocados métodos de autoavaliação ergonómica, nos quais o risco é percecionado pelo próprio trabalhador; observacionais, conduzidos por peritos em ergonomia; e de medição direta, que recorrem ao emprego de soluções tecnológicas para a recolha e monitorização objetiva de variáveis pertinentes para a avaliação ergonómica. Porém, frequentemente e em contexto industrial, são apenas aplicados métodos de autoavaliação e observacionais, apesar da medição direta constituir uma solução mais notável. O advento da Internet das Coisas vem revelar a oportunidade da utilização de wearables para uma recolha de dados omnipresente, amplificando a quantidade de dados disponível com o fim de uma avaliação ergonómica mais individual e imparcial. Deste modo, estudos relativos à avaliação ergonómica no local de trabalho têm primado pelo uso de wearables com vista a monitorização do movimento humano. A presente dissertação respeita ao desenvolvimento de uma abordagem automática para a avaliação ergonómica em contexto industrial. As contribuições principais são o desenvolvimento de (1) uma rotina de captura de movimento, através da utilização de um sistema wearable com sensores inerciais; (2) uma framework computacional para a monitorização do movimento da parte superior do corpo humano, em termos dos ângulos relativos às articulações entre os segmentos anatómicos, estimados com recurso à cinemática inversa; e (3) implementações computacionais de especificações estabelecidas e relativas aos fatores de risco de postura para a quantificação da exposição biomecânica e consequente risco ergonómico em âmbito ocupacional. Subsequentemente, as implementações das especificações foram aplicadas por forma a prover constatações acerca de um caso de estudo das linhas de montagem de automóveis da Volkswagen Autoeuropa. O estudo delineado foi dividido em dois cenários: validação e avaliação. A validação consistiu em comparar os dados provisionados por um sistema inercial de referência e determinados através dos métodos desenvolvidos. Para tal, usaram-se dados de sensores inerciais recolhidos em laboratório (N = 8 participantes) e nas linhas de montagem de automóveis (N = 9 participantes). A avaliação consistiu em quantificar a exposição biomecânica e consequente risco ergonómico respeitantes ao caso de estudo, empregando as estimativas angulares calculadas pela framework desenvolvida, e a partir dos dados recolhidos com o nosso sistema nas linhas de montagem de automóveis. Os resultados revelaram que a framework proposta tem o potencial para ser aplicada na monitorização de tarefas industriais. A avaliação ergonómica é mais lata através da medição direta, desvendando diferenças de exposição biomecânica e consequente risco ergonómico entre operadores.Work-related musculoskeletal disorders (WRMSDs) represent 15% of the total number of life-years lost due to work-related injuries and illness. Among WRMSDs’ risk factors, work-related postures are underlined in this research. Biomechanical exposure to hazardous postures negatively impacts workers’ health, enterprises’ economy, and society. Toward the apperception about the recurrent practice of hazardous postures in the workplace, self-reported, observational, and directly measured ergonomic assessment methods have been established. However, only self-reported and observational approaches are enforced on a more frequent basis, besides directly measured is a more compelling choice. The advent of the Internet of Things poses the opportunity of using wearables in the direction of ubiquitous data collection, increasing the amount of available data for a more personal and non-biased ergonomic evaluation. As follows, over workplace ergonomics research, wearables have been used to monitor human motion. The dissertation developed an automatic approach to ergonomic evaluation in industrial contexts. Its main contributions are the development of (1) a motion capture routine using inertial sensors; (2) a computational framework to monitor human upper body motion, in terms of joints’ angles, through inverse kinematics; and (3) computational implementations of posture risk factors specifications to quantify the biomechanical exposure and consequent ergonomic risk in occupational settings. Subsequently, specifications implementations were applied to provide insights in consideration of a case study from Volkswagen Autoeuropa automotive assembly lines. The research was divided into two scenarios: validation and evaluation. Validation consisted of comparing data provided by a ground truth inertial motion capture system and computed throughout the developed methods. Hence, inertial sensors’ data, collected in the laboratory (N = 8 participants) and automotive assembly lines (N = 9 participants) settings, were used. The evaluation consisted of quantifying the biomechanical exposure and consequent ergonomic risk concerning the case study, using angular estimates computed through the developed framework and about data collected in automotive assembly lines. The results revealed that the proposed framework has the potential to be applied to monitor industrial tasks. The ergonomic evaluation is more comprehensive through direct measures, uncovering differences about biomechanical exposure and consequent ergonomic risk among operators