Sensor-based human activity mining using Dirichlet process mixtures of directional statistical models

Funding: UK EPSRC under grant number EP/N007565/1, “Science of Sensor Systems Software”.We have witnessed an increasing number of activity-aware applications being deployed in real-world environments, including smart home and mobile healthcare. The key enabler to these applications is sensor-based human activity recognition; that is, recognising and analysing human daily activities from wearable and ambient sensors. With the power of machine learning we can recognise complex correlations between various types of sensor data and the activities being observed. However the challenges still remain: (1) they often rely on a large amount of labelled training data to build the model, and (2) they cannot dynamically adapt the model with emerging or changing activity patterns over time. To directly address these challenges, we propose a Bayesian nonparametric model, i.e. Dirichlet process mixture of conditionally independent von Mises Fisher models, to enable both unsupervised and semi-supervised dynamic learning of human activities. The Bayesian nonparametric model can dynamically adapt itself to the evolving activity patterns without human intervention and the learning results can be used to alleviate the annotation effort. We evaluate our approach against real-world, third-party smart home datasets, and demonstrate significant improvements over the state-of-the-art techniques in both unsupervised and supervised settings.Postprin

Bayesian methodologies for constrained spaces.

Due to advances in technology, there is a presence of directional data in a wide variety of fields. Often distributions to model directional data are defined on manifold or constrained spaces. Regular statistical methods applied to data defined on special geometries can give misleading results, and this demands new statistical theory. This dissertation addresses two such problems and develops Bayesian methodologies to improve inference in these arenas. It consists of two projects: 1. A Bayesian Methodology for Estimation for Sparse Canonical Correlation, and 2. Bayesian Analysis of Finite Mixture Model for Spherical Data. In principle, it can be challenging to integrate data measured on the same individuals occurring from different experiments and model it together to gain a larger understanding of the problem. Canonical Correlation Analysis (CCA) provides a useful tool for establishing relationships between such data sets. When dealing with high dimensional data sets, Structured Sparse CCA (ScSCCA) is a rapidly developing methodological area which seeks to represent the interrelations using sparse direction vectors for CCA. There is less development in Bayesian methodology in this area. We propose a novel Bayesian ScSCCA method with the use of a Bayesian infinite factor model. Using a multiplicative half Cauchy prior process, we bring in sparsity at the level of the projection matrix. Additionally, we promote further sparsity in the covariance matrix by using graphical horseshoe prior or diagonal structure. We compare the results for our proposed model with competing frequentist and Bayesian methods and apply the developed method to omics data arising from a breast cancer study. In the second project, we perform Bayesian Analysis for the von Mises Fisher (vMF) distribution on the sphere which is a common and important distribution used for directional data. In the first part of this project, we propose a new conjugate prior for the mean vector and concentration parameter of the vMF distribution. Further we prove its properties like finiteness, unimodality, and provide interpretations of its hyperparameters. In the second part, we utilize a popular prior structure for a mixture of vMF distributions. In this case, the posterior of the concentration parameter consists of an intractable Bessel function of the first kind. We propose a novel Data Augmentation Strategy (DAS) using a Negative Binomial Distribution that removes this intractable Bessel function. Furthermore, we apply the developed methodology to Diffusion Tensor Imaging (DTI) data for clustering to explore voxel connectivity in human brain

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM)

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Multitarget Tracking Using Orientation Estimation for Optical Belt Sorting

In optical belt sorting, accurate predictions of the bulk material particles’ motions are required for high-quality results. By implementing a multitarget tracker tailored to the scenario and deriving novel motion models, the predictions are greatly enhanced. The tracker’s reliability is improved by also considering the particles’ orientations. To this end, new estimators for directional quantities based on orthogonal basis functions are presented and shown to outperform the state of the art

Master of Science

thesisTotal knee arthroplasty (TKA) is the gold-standard treatment for degenerative and arthritic knee diseases. TKA replaces the damaged knee articular surfaces with a prosthetic knee joint composed of a metal femoral component and polyethylene tibial insert. In 2013, approximately 650,000 primary TKA procedures were performed in the U.S., with approximately 10% requiring revision surgery necessitated by the 10 - 15 years limited lifetime of the prosthetic knee joint. A major limiting factor to the longevity of a prosthetic knee joint is fatigue crack damage of the tibial insert. The objective of this work is to address the problem of fatigue crack damage through: (1) experimentally quantifying fatigue crack damage in polyethylene tibial inserts and (2) predicting fatigue crack damage through finite element modeling. We have developed a novel subsurface fatigue crack damage measurement method based on specimen transillumination and used this method to measure fatigue crack damage in two tibial inserts. We have also developed a dynamic finite element simulation of the stress in the tibial insert under knee simulator wear test conditions, for an entire gait cycle. Two polyethylene material models, linear elastic and linear viscoelastic, were compared. It was observed that choice of material model has a substantial effect on the maximum von Mises stress. The location of maximum von Mises, principal, and shear stress in the tibial insert were compared to the experimentally measured fatigue crack damage to determine whether the simulation accurately predicts fatigue crack damage in the tibial insert. It was observed that the von Mises stress alone is a poor predictor of fatigue crack damage, while the locations of maximum tensile principal stress and shear stress correspond closely to the locations where fatigue crack damage occurred

Improving flow-induced hemolysis prediction models.

Partial or complete failure of red blood cell membrane, also known as hemolysis, is a persistent issue with almost all blood contacting devices. Many experimental and theoretical contributions over the last few decades have increased insight into the mechanisms of mechanical hemolysis in both laminar and turbulent flow regimes, with the ultimate goal of developing a comprehensive, mechanistic and universal hemolysis prediction model. My research is broadly divided into two sections: theoretical/analytical/Computational Fluid Dynamics (CFD) analyses and experimental tests. The first part of my research revolved entirely around analyzing the simplest and most popular hemolysis model, commonly called as the power-law model. This model was developed only for laminar pure shear flow within a limited range of exposure time. Subsequently, modified versions of this model have been developed to be used for more complex flows. Many of these modified models assume that hemolysis scales with a resultant, scalar stress representing all components of the fluid stress tensor. The most common representative stress used in the power-law model is a von-Mises-like stress. However, using membrane tension models for pure shear and pure extension in both laminar and turbulent flows, for some simple example cases, we have shown that scalar stress alone is inadequate for scaling hemolysis. Alternatively, the rate of viscous energy dissipation rate has also been proposed as the parameter to scale hemolysis with. Applying the same order-of-magnitude estimate as vi mentioned above, we have found that dissipation rate even behaves worse than the resultant scalar stress for hemolysis prediction. It is therefore concluded that energy dissipation rate alone is also not sufficient to universally scale blood damage across complex flows. These show that a realistic model of hemolysis must take into account different responses of the viscoelastic cell membrane to different stress type. Various discretized version of the power-law model has also been introduced for post-processing of the CFD results. The power law can be either discretized in space, Eulerian treatment, or in time, Lagrangian treatment. Our study on the Eulerian approach revealed that the current equations used in the literature has a missing term, and thus incorrect. We also examined the mathematical stability of the discretized power-law model, and found that it may introduce significant error in red cell damage prediction for certain pathlines with specific stress history. Experimental results on deformation of red cell in pure shear flow is present for a relatively wide range of shear rates. However, red cell deformation/elongation in pure laminar extensional flow is scarce, with only one publication reporting their results on red cell deformation for only up to stress level of 10 Pa. For the experimental part of my research, we conducted experiments to observe the difference in deformation of red cell in pure shear and pure extensional flows, for stresses beyond what has already been reported in the literature. This dissertation is composed of three chapters. Chapter I is the literature survey and introductory materials. Chapter II contains the discussion and results for the theoretical/analytical/CFD part of the research. Finally, discussion and results for the experimental tests are presented in Chapter III