General geometry of belief function combination

In this paper we build on previous work on the geometry of Dempster’s rule to investigate the geometric behaviour of various other combination rules, including Yager’s, Dubois’, and disjunctive combination, starting from the case of binary frames of discernment. Believability measures for unnormalised belief functions are also considered. A research programme to complete this analysis is outlined

A belief-theoretical approach to example-based pose estimation

​In example-based human pose estimation, the configuration of an evolving object is sought given visual evidence, having to rely uniquely on a set of sample images. We assume here that, at each time instant of a training session, a number of feature measurements is extracted from the available images, while ground truth is provided in the form of the true object pose. In this scenario, a sensible approach consists in learning maps from features to poses, using the information provided by the training set. In particular, multi-valued mappings linking feature values to set of training poses can be constructed. To this purpose we propose a Belief Modeling Regression (BMR) approach in which a probability measure on any individual feature space maps to a convex set of probabilities on the set of training poses, in a form of a belief function. Given a test image, its feature measurements translate into a collection of belief functions on the set of training poses which, when combined, yield there an entire family of probability distributions. From the latter either a single central pose estimate or a set of extremal ones can be computed, together with a measure of how reliable the estimate is. Contrarily to other competing models, in BMR the sparsity of the training samples can be taken into account to model the level of uncertainty associated with these estimates. We illustrate BMR’s performance in an application to human pose recovery, showing how it outperforms our implementation of both Relevant Vector Machine and Gaussian Process Regression. Finally, we discuss motivation and advantages of the proposed approach with respect to its most direct competitors

Metric learning for Parkinsonian identification from IMU gait measurements

Diagnosis of people with mild Parkinson’s symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10 metre walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation. Given sufficient training samples, the distance between HMMs which optimises classification performance is learned and employed in a classical Nearest Neighbour classifier. Our tests demonstrate how this technique achieves accuracy of 85.51% over a 156 people with Parkinson’s with a representative range of severity and 424 typically developed adults, which is the top performance achieved so far over a cohort of such size, based on single measurement outcomes. The method displays the potential for further improvement and a wider application to distinguish other conditions

Alternative formulations of the theory of evidence based on basic plausibility and commonality assignments

In this paper we introduce indeed two alternative formulations of the theory of evidence by proving that both plausibility and commonality functions share the same combinatorial structure of sum function of belief functions, and computing their Moebius inverses called basic plausibility and commonality assignments. The equivalence of the associated formulations of the ToE is mirrored by the geometric congruence of the related simplices. Applications to the probabilistic approximation problem are briefly presented

The recovery umbrella in the world of elite sport: Do not forget the coaching and performance staff

In the field of sports science, the recovery umbrella is a trending topic, and even more so in the world of elite sports. This is evidenced by the significant increase in scientific publications during the last 10 years as teams look to find a competitive edge. Recovery is recognized to be an integral component to assist athlete preparation in the restoration of physical and psychological function, and subsequently, performance in elite team sports athletes. However, the importance of recovery in team staff members (sports coaches and performance staff) in elite sports appears to be a forgotten element. Given the unrelenting intense nature of daily tasks and responsibilities of team staff members, the elite sports environment can predispose coaches to increased susceptibility to psycho-socio physiological fatigue burden, and negatively affect health, wellbeing, and performance. Therefore, the aim of this opinion was to (1) develop an educational recovery resource for team staff members, (2) identify organizational task-specific fatigue indicators and barriers to recovery and self-care in team staff members, and (3) present recovery implementation strategies to assist team staff members in meeting their organizational functions. It is essential that we do not forget the coaching and performance staff in the recovery process. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Dual properties of the relative belief of singletons

In this paper we prove that a recent Bayesian approximation of belief functions, the relative belief of singletons, meets a number of properties with respect to Dempster’s rule of combination which mirrors those satisfied by the relative plausibility of singletons. In particular, its operator commutes with Dempster’s sum of plausibility functions, while perfectly representing a plausibility function when combined through Dempster’s rule. This suggests a classification of all Bayesian approximations into two families according to the operator they relate to

'Asking the right question'. A comparison of two approaches to gathering data on 'herbals' use in survey based studies

BACKGROUND:Over the last decade academic interest in the prevalence and nature of herbal medicines use by pregnant women has increased significantly. Such data are usually collected by means of an administered questionnaire survey, however a key methodological limitation using this approach is the need to clearly define the scope of 'herbals' to be investigated. The majority of published studies in this area neither define 'herbals' nor provide a detailed checklist naming specific 'herbals' and CAM modalities, which limits inter-study comparison, generalisability and the potential for meta-analyses. The aim of this study was to compare the self-reported use of herbs, herbal medicines and herbal products using two different approaches implemented in succession. METHODS:Cross-sectional questionnaire surveys of women attending for their mid-trimester scan or attending the postnatal unit following live birth at the Royal Aberdeen Maternity Hospital, North-East Scotland. The questionnaire utilised two approaches to collect data on 'herbals' use, a single closed yes/no answer to the question "have you used herbs, herbal medicines and herbal products in the last three months"; and a request to tick which of a list of 40 'herbals' they had used in the same time period. RESULTS:A total of 889 responses were obtained of which 4.3% (38) answered 'yes' to herbal use via the closed question. However, using the checklist 39% (350) of respondents reported the use of one or more specific 'herbals' (p<0.0001). The 312 respondents who reported 'no' to 'herbals' use via the closed question but "yes" via the checklist consumed a total of 20 different 'herbals' (median 1, interquartile range 1-2, range 1-6). CONCLUSIONS:This study demonstrates that the use of a single closed question asking about the use of 'herbals', as frequently reported in published studies, may not yield valid data resulting in a gross underestimation of actual use

Generalised max entropy classifiers

In this paper we propose a generalised maximum-entropy classification framework, in which the empirical expectation of the feature functions is bounded by the lower and upper expectations associated with the lower and upper probabilities associated with a belief measure. This generalised setting permits a more cautious appreciation of the information content of a training set. We analytically derive the KarushKuhn-Tucker conditions for the generalised max-entropy classifier in the case in which a Shannon-like entropy is adopted