Tracking and modelling motion for biomechanical analysis

This thesis focuses on the problem of determining appropriate skeletal configurations for which a virtual animated character moves to desired positions as smoothly, rapidly, and as accurately as possible. During the last decades, several methods and techniques, sophisticated or heuristic, have been presented to produce smooth and natural solutions to the Inverse Kinematics (IK) problem. However, many of the currently available methods suffer from high computational cost and production of unrealistic poses. In this study, a novel heuristic method, called Forward And Backward Reaching Inverse Kinematics (FABRIK), is proposed, which returns visually natural poses in real-time, equally comparable with highly sophisticated approaches. It is capable of supporting constraints for most of the known joint types and it can be extended to solve problems with multiple end effectors, multiple targets and closed loops. FABRIK was compared against the most popular IK approaches and evaluated in terms of its robustness and performance limitations. This thesis also includes a robust methodology for marker prediction under multiple marker occlusion for extended time periods, in order to drive real-time centre of rotation (CoR) estimations. Inferred information from neighbouring markers has been utilised, assuming that the inter-marker distances remain constant over time. This is the first time where the useful information about the missing markers positions which are partially visible to a single camera is deployed. Experiments demonstrate that the proposed methodology can effectively track the occluded markers with high accuracy, even if the occlusion persists for extended periods of time, recovering in real-time good estimates of the true joint positions. In addition, the predicted positions of the joints were further improved by employing FABRIK to relocate their positions and ensure a fixed bone length over time. Our methodology is tested against some of the most popular methods for marker prediction and the results confirm that our approach outperforms these methods in estimating both marker and CoR positions. Finally, an efficient model for real-time hand tracking and reconstruction that requires a minimum number of available markers, one on each finger, is presented. The proposed hand model is highly constrained with joint rotational and orientational constraints, restricting the fingers and palm movements to an appropriate feasible set. FABRIK is then incorporated to estimate the remaining joint positions and to fit them to the hand model. Physiological constraints, such as inertia, abduction, flexion etc, are also incorporated to correct the final hand posture. A mesh deformation algorithm is then applied to visualise the movements of the underlying hand skeleton for comparison with the true hand poses. The mathematical framework used for describing and implementing the techniques discussed within this thesis is Conformal Geometric Algebra (CGA)

Visualising Human Motion: a First Principles Approach using Vicon data in Maya

Author version made available in accordance with publisher copyright policy.This paper describes a first principles approach to understanding how 3D digital animation of human motion can be processed and produced from raw data, without the use of proprietary software. The paper describes how students collected motion data using a custom marker set, how this was used to create a point cloud, how errors were corrected, and finally how the skeleton was rigged, skinned and modelled in Maya

Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function

Design and Test of a Biomechanical Model for the Estimation of Knee Joint Angle During Indoor Rowing: Implications for FES-Rowing Protocols in Paraplegia

Functional electrical stimulation of lower limb muscles during rowing provides a means for the cardiovascular conditioning in paraplegia. The possibility of shaping stimulation profiles according to changes in knee angle, so far conceived as changes in seat position, may help circumventing open issues associated with muscle fatigue and movement coordination.Here we present a subject-specific biomechanical model for the estimation of knee joint angle during indoor rowing. Anthropometric measurements and foot and seat position are inputs to the model. We tested our model on two samples of elite rowers; 15 able-bodied and 11 participants in the Rio 2016 Paralympic games. Paralympic rowers presented minor physical disabilities (LTA-PD classification), enabling them to perform the full rowing cycle (with legs, trunks and arms). Knee angle was estimated from the rowing machine seat position, measured with a linear encoder and transmitted wirelessly to a computer. Key results indicate the root mean square error (RMSE) between estimated and measured angles did not depend on group and stroke rate (p>0.267). Significantly greater RMSE values were observed however within the rowing cycle (p<0.001), reaching on average 8deg in the mid-recovery phase. Differences between estimated and measured knee angle values resulted in slightly earlier (5%) detection of knee flexion, regardless of the group and stroke rate considered. Offset of knee extension, knee angle at catch and range of knee motion were identified equally well with our model and with inertial sensors. These results suggest our model describes accurately the movement of knee joint during indoor rowing

Personalised antimicrobial management in secondary care

Background: The growing threat of Antimicrobial Resistance (AMR) requires innovative methods to promote the sustainable effectiveness of antimicrobial agents. Hypothesis: This thesis aimed to explore the hypothesis that personalised decision support interventions have the utility to enhance antimicrobial management across secondary care. Methods: Different research methods were used to investigate this hypothesis. Individual physician decision making was mapped and patient experiences of engagement with decision making explored using semi-structured interviews. Cross-specialty engagement with antimicrobial management was investigated through cross-sectional analysis of conference abstracts and educational training curricula. Artificial intelligence tools were developed to explore their ability to predict the likelihood of infection and provide individualised prescribing recommendations using routine patient data. Dynamic, individualised dose optimisation was explored through: (i) development of a microneedle based, electrochemical biosensor for minimally invasive monitoring of beta-lactams; and (ii) pharmacokinetic (PK)-pharmacodynamic (PD) modelling of a new PK-PD index using C-Reactive protein (CRP) to predict the pharmacodynamics of vancomycin. Ethics approval was granted for all aspects of work explored within this thesis. Results: Mapping of individual physician decision making during infection management demonstrated several areas where personalised, technological interventions could enhance antimicrobial management. At specialty level, non-infection specialties have little engagement with antimicrobial management. The importance of engaging surgical specialties, who have relatively high rates of antimicrobial usage and healthcare associated infections, was observed. An individualised information leaflet, co-designed with patients, to provide personalised infection information to in-patients receiving antibiotics significantly improved knowledge and reported engagement with decision making. Artificial intelligence was able to enhance the prediction of infection and the prescribing of antimicrobials using routinely available clinical data. Real-time, continuous penicillin monitoring was demonstrated using a microneedle based electrochemical sensor in-vivo. A new PK-PD index, using C-Reactive Protein, was able to predict individual patient response to vancomycin therapy at 96-120 hours of therapy. Conclusion: Through co-design and the application of specific technologies it is possible to provide personalised antimicrobial management within secondary care.Open Acces

A novel monitoring system for the training of elite swimmers

Swimming performance is primarily judged on the overall time taken for a swimmer to complete a specified distance performing a stroke that complies with current regulations defined by the Fédération Internationale de Natation (FINA), the International governing body of swimming. There are three contributing factors to this overall time; the start, free swimming and turns. The contribution of each of these factors is event dependent; for example, in a 50m event there are no turns, however, the start can be a significant contributor. To improve overall performance each of these components should be optimised in terms of skill and execution. This thesis details the research undertaken towards improving performance-related feedback in swimming. The research included collaboration with British Swimming, the national governing body for swimming in the U.K., to drive the requirements and direction of research. An evaluation of current methods of swimming analysis identified a capability gap in real-time, quantitative feedback. A number of components were developed to produce an integrated system for comprehensive swim performance analysis in all phases of the swim, i.e. starts, free swimming and turns. These components were developed to satisfy two types of stakeholder requirements. Firstly, the measurement requirements, i.e. what does the end user want to measure? Secondly, the process requirements, i.e. how would these measurements be achieved? The components developed in this research worked towards new technologies to facilitate a wider range of measurement parameters using automated methods as well as the application of technologies to facilitate the automation of current techniques. The development of the system is presented in detail and the application of these technologies is presented in case studies for starts, free swimming and turns. It was found that developed components were able to provide useful data indicating levels of performance in all aspects of swimming, i.e. starts, free swimming and turns. For the starts, an integrated solution of vision, force plate technology and a wireless iii node enabled greater insight into overall performance and quantitative measurements of performance to be captured. Force profiles could easily identify differences in swimmer ability or changes in technique. The analysis of free swimming was predominantly supported by the wireless sensor technology, whereby signal analysis was capable of automatically determining factors such as lap times variations within strokes. The turning phase was also characterised in acceleration space, allowing the phases of the turn to be individually assessed and their contribution to total turn time established. Each of the component technologies were not used in isolation but were supported by other synchronous data capture. In all cases a vision component was used to increase understanding of data outputs and provide a medium that coaches and athletes were comfortable with interpreting. The integrated, component based system has been developed and tested to prove its ability to produce useful, quantitative feedback information for swimmers. The individual components were found to be capable of providing greater insight into swimming performance, that has not been previously possible using the current state of the art techniques. Future work should look towards the fine-tuning of the prototype system into a useable solution for end users. This relies on the refinement of components and the development of an appropriate user interface to enable ease of data collection, analysis, presentation and interpretation