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

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

Explaining the Ergonomic Assessment of Human Movement in Industrial Contexts

Manufacturing processes are based on human labour and the symbiosis between human operators and machines. The operators are required to follow predefined sequences of movements. The operations carried out at assembly lines are repetitive, being identified as a risk factor for the onset of musculoskeletal disorders. Ergonomics plays a big role in preventing occupational diseases. Ergonomic risk scores measure the overall risk exposure of operators however these methods still present challenges: the scores are often associated to a given workstation, being agnostic to the variability among operators. Observation methods are most often employed yet require a significant amount of effort, preventing an accurate and continuous ergonomic evaluation to the entire population of operators. Finally, the risk’s results are rendered as index scores, hindering a more comprehensive interpretation by occupational physicians. This dissertation developed a solution for automatic operator risk exposure in assembly lines. Three main contributions were presented: (1) an upper limb and torso motion tracking algorithm which relies on inertial sensors to estimate the orientation of anatomical joints; (2) an adjusted ergonomic risk score; (3) an ergonomic risk explanation approach based on the analysis of the angular risk factors. Throughout the research, two experimental assessments were conducted: laboratory validation and field evaluation. The laboratory tests enabled the creation of a movements’ dataset and used an optical motion capture system as reference. The field evaluation dataset was acquired on an automotive assembly line and serve as the basis for an ergonomic risk evaluation study. The experimental results revealed that the proposed solution has the potential to be applied in a real environment. Through direct measures, the ergonomic feedback is fastened, and consequently, the evaluation can be extended to more operators, ultimately preventing, in long-term, work-related injuries

Coupled Rigid Body Dynamics with Application to Diving

Platform and springboard diving is a sport involving athletes falling or jumping into a pool of water, usually while performing acrobatic manoeuvres. At the highest level it challenges the physical laws of gravity as athletes try to outperform each other by executing more sophisticated dives. With a mathematical model we are able to assist the athletes and coaches by providing some insight into the mechanics of diving, which hopefully gives them an edge during competition. In this thesis we begin with an introduction to rigid body dynamics and then extend the results to coupled rigid bodies. We generalise Euler's equations of motion and equations of orientation for rigid bodies to be applicable for coupled rigid bodies. The athlete is represented as a mathematical model consisting of ten simple geometric solids, which is used to conduct three projects within this thesis. In the first project we look at somersaults without twists, which provides a significant reduction as the model becomes planar. The equations of motion and equations of orientation reduce from vector form to a single scalar differential equation for orientation, since angular momentum is conserved. We digitise footage of an elite diver performing 107B (forward 3.5 somersault in pike) from the 3m springboard, and feed that data into our model for comparison between the theoretically predicted and observed result. We show that the overall rotation obtained by the athlete through somersault is composed of two parts, the major contribution coming from the dynamic phase and a small portion from the geometric phase. We note that by modifying the digitised dive slightly we can leave the dynamic phase intact, but change the geometric phase to provide a small boost in overall rotation. The technique involved in doing so is not practical for actual diving though, so we move away from this idea and devise another way of optimising for the overall rotation. We find that by shape changing in a particular way that takes slightly longer than the fastest way of moving into and out of pike, the overall rotation achieved can be improved by utilising the geometric phase. In the second project we use the model to simulate divers performing forward m somersaults with n twists. The formulas derived are general, but we will specifically look at 5132D, 5134D, 5136D, and 5138D (forward 1.5 somersaults with 1, 2, 3, and 4 twists) dives. To keep the simulation as simple as possible we reduce the segment count to two by restricting the athlete to only using their left arm about the abduction-adduction plane of motion. We show how twisting somersaults can be achieved in this manner using this simple model with predetermined set of motor actions. The dive mechanics consist of the athlete taking off in pure somersaulting motion, executing a shape change mid-flight to get into twist position, perform twisting somersaults in rigid body motion, and then executing another shape change to revert the motion back into pure somersaulting motion to complete the dive. In the third and final project we use our model to show how a 513XD dive (forward 1.5 somersaults with 5 twists) is performed. This complicated dive differs from all currently performed dives in that once the diver initiates twist in the somersaulting motion via shape change, they need to perform another appropriately timed shape change to speed up the twist rather than stopping the twist, and only then is five twists obtainable with practical parameters. Such techniques can be found in aerial skiing where the airborne time is longer, but our theory shows that it may also be applicable to platform and springboard diving too. To date, no athlete has ever attempted a 513XD in competition, nor does the International Swimming Federation (FINA) cover dives with five twists in their degree-of-difficulty formula. Our theory shows that 513XD dive is theoretically possible, and with extrapolation we estimate it would have a degree-of-difficulty of 3.9

Towards Remote Gait Analysis: Combining Physics and Probabilistic Models for Estimating Human Joint Mechanics

The connected health movement and remote patient monitoring promise to revolutionize patient care in multiple clinical contexts. In orthopedics, continuous monitoring of human joint and muscle tissue loading in free-living conditions will enable novel insight concerning musculoskeletal disease etiology. These developments are necessary for comprehensive patient characterization, progression monitoring, and personalized therapy. This vision has motivated many recent advances in wearable sensor-based algorithm development that aim to perform biomechanical analyses traditionally restricted to confined laboratory spaces. However, these techniques have not translated to practical deployment for remote monitoring. Several barriers to translation have been identified including complex sensor arrays. Thus, the aim of this work was to lay the foundation for remote gait analysis and techniques for estimating clinically relevant biomechanics with a reduced sensor array. The first step in this process was to develop an open-source platform that generalized the processing pipeline for automated remote biomechanical analysis. The clinical utility of the platform was demonstrated for monitoring patient gait following knee surgery using continuous recordings of thighworn accelerometer data and rectus femoris electromyograms (EMG) during free-living conditions. Individual walking bouts were identified from which strides were extracted and characterized for patient evaluation. A novel, multifactorial asymmetry index was proposed based on temporal, EMG, and kinematic descriptors of gait that was able to differentiate between patients at different stages of recovery and that was more sensitive to recovery time than were indices of cumulative physical activity. The remainder of the work focused on algorithms for estimating joint moment and simulating muscle contraction dynamics using a reduced sensor array. A hybrid technique was proposed that combined both physics and probabilistic models in a complementary fashion. Specifically, the notion of a muscle synergy function was introduced that describes the mapping between excitations from a subset of muscles and excitations from other synergistic muscles. A novel model of these synergy functions was developed that enabled estimation of unmeasured muscle excitations using a measured subset. Data from thigh- and shank-worn inertial sensors were used to estimate segment kinematics and muscle-tendon unit (MTU) lengths using physics-based techniques and a model of the musculoskeletal geometry. These estimates of muscle excitation and MTU length were used as inputs for EMG-driven simulation of muscle contraction. Estimates of muscle force, power, and work as well as net joint moment from the proposed hybrid technique were compared to estimates from laboratory-based techniques. This presents the first sensor-only (four EMG and two inertial sensors) simulation of muscle contraction dynamics and joint moment estimation using machine learning only for estimating unmeasured muscle excitations. This work provides the basis for automated remote biomechanical analysis with reduced sensor arrays; from raw sensor recordings to estimates of muscle moment, force, and power. The proposed hybrid technique requires data from only four EMG and two inertial sensors and work has begun to seamlessly integrate these sensors into a knee brace for monitoring patients following knee surgery. Future work should build on these developments including further validation and design of methods utilizing remotely and longitudinally observed biomechanics for prognosis and optimizing patient-specific interventions

Improving the validity of shod human footstrike modelling with dynamic loading conditions determined from biomechanical motion capture trials

This thesis presents and evaluates a number of finite element footstrike models developed to allow the performance of prospective athletic footwear designs to be evaluated in a virtual environment. Successful implementation of such models would reduce the industry’s traditional reliance on physical prototyping and therefore reduce the time and associated costs required to develop a product. All boundary conditions defined in each of the footstrike models reported were directly determined from biomechanical motion capture trials to ensure that the loading applied was representative of shod human running. Similarly, the results obtained with each model were compared to digitised high speed video footage of experimental trials and validated against biomechanical measures such as foot segment kinematics, ground reaction force and centre of pressure location. A simple model loaded with triaxial force profiles determined from the analysis of plantar pressure data was found to be capable of applying highly representative load magnitudes but the distribution of applied loading was found to be less accurate. Greater success at emulating the deformation that occurs in the footwear during an entire running footstrike was achieved with models employing kinematic foot segment boundary conditions although this approach was found to be highly sensitive to the initial orientation of the foot and footwear components, thus limiting the predictive capacity of such a methodology. A subsequent model was therefore developed to utilise exclusively kinetic load conditions determined from an inverse dynamic analysis of an experimental trial and demonstrated the greatest predictive capacity of all reported models. This was because the kinematics of the foot were allowed to adapt to the footwear conditions defined in the analysis with this approach. Finally, the reported finite element footstrike models were integrated with automated product optimisation techniques. A topology optimisation approach was first utilised to generate lightweight midsole components optimised for subject‐specific loading conditions whilst a similar shape optimisation methodology was subsequently used to refine the geometry of a novel footwear design in order to minimise the peak material strains predicted