VISION-BASED URBAN NAVIGATION PROCEDURES FOR VERBALLY INSTRUCTED ROBOTS

The work presented in this thesis is part of a project in instruction based learning (IBL) for mobile robots were a robot is designed that can be instructed by its users through unconstrained natural language. The robot uses vision guidance to follow route instructions in a miniature town model. The aim of the work presented here was to determine the functional vocabulary of the robot in the form of "primitive procedures". In contrast to previous work in the field of instructable robots this was done following a "user-centred" approach were the main concern was to create primitive procedures that can be directly associated with natural language instructions. To achieve this, a corpus of human-to-human natural language instructions was collected and analysed. A set of primitive actions was found with which the collected corpus could be represented. These primitive actions were then implemented as robot-executable procedures. Natural language instructions are under-specified when destined to be executed by a robot. This is because instructors omit information that they consider as "commonsense" and rely on the listener's sensory-motor capabilities to determine the details of the task execution. In this thesis the under-specification problem is solved by determining the missing information, either during the learning of new routes or during their execution by the robot. During learning, the missing information is determined by imitating the commonsense approach human listeners take to achieve the same purpose. During execution, missing information, such as the location of road layout features mentioned in route instructions, is determined from the robot's view by using image template matching. The original contribution of this thesis, in both these methods, lies in the fact that they are driven by the natural language examples found in the corpus collected for the IDL project. During the testing phase a high success rate of primitive calls, when these were considered individually, showed that the under-specification problem has overall been solved. A novel method for testing the primitive procedures, as part of complete route descriptions, is also proposed in this thesis. This was done by comparing the performance of human subjects when driving the robot, following route descriptions, with the performance of the robot when executing the same route descriptions. The results obtained from this comparison clearly indicated where errors occur from the time when a human speaker gives a route description to the time when the task is executed by a human listener or by the robot. Finally, a software speed controller is proposed in this thesis in order to control the wheel speeds of the robot used in this project. The controller employs PI (Proportional and Integral) and PID (Proportional, Integral and Differential) control and provides a good alternative to expensive hardware

Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

Epilepsy is a neurological disorder that affects 50 million people worldwide. It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Wearable electronic devices that detect seizures have the potential to hail timely assistance for individuals, inform their treatment, and assist care and self-management. This systematic review encompasses the literature relevant to the evaluation of wearable electronics for epilepsy. Devices and performance metrics are identified, and the evaluations, both quantitative and qualitative, are presented. Twelve primary studies comprising quantitative evaluations from 510 patients and participants were collated according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Two studies (with 104 patients/participants) comprised both qualitative and quantitative evaluation components. Despite many works in the literature proposing and evaluating novel and incremental approaches to seizure detection, there is a lack of studies evaluating the devices available to consumers and researchers, and there is much scope for more complete evaluation data in quantitative studies. There is also scope for further qualitative evaluations amongst individuals, carers, and healthcare professionals regarding their use, experiences, and opinions of these devices

Data Mining for Learning Analytics: does lack of engagement always mean what we think it does?

Context and Objectives Learning Analytics (LA) has the potential to utilise student data to further the advancement of a personalized, supportive system of HE (Johnson et al., 2013). A number of LA systems are now being developed but there have been few studies that have analysed the usage of Virtual Learning Environments (VLE) in order to identify which analytics techniques and sources of data accurately reflect student engagement and achievement. Methods The interactions of 66 students with a Level 4 programming module on a VLE have been analysed via the simple K-means clustering algorithm to identify classes of behaviour and their characteristics. Results Two prominent classes were found with students achieving higher marks attending the lectures and tutorials more regularly and accessing all types of material on the VLE more frequently than students in the lower achieving cluster. However, there were a number of exceptions that had low levels of engagement that gained high marks and vice versa. Discussion A student’s prior experience and characteristics of their degree programme need to be taken into account to avoid incorrectly interpreting high and low levels of engagement. Conclusions The number of times students view online module materials will be an important factor for inclusion in any predictive LA models but must be able to take into account the differences in student backgrounds, delivery styles and subject

Feasibility of using combined EMG and kinematic signals for prosthesis control : A simulation study using a virtual reality environment

Acknowledgment This study was partly supported by a UK Medical Research Council Centenary Award to Keele University.Peer reviewedPublisher PD

Improving predictor selection for injury modelling methods in male footballers

This study evaluated whether combining existing methods of Elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real life applicability of injury prediction models in football. Predictor selection and model development was conducted on a pre-existing dataset, from a single English football teams’ 2015/2016 season. The Elastic Net for zero-inflated Poisson penalty method was successful shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, i.e. mass and body fat content, training type, duration and surface, fitness levels, normalised period of “no-play” and time in competition could contribute to the probability of acquiring a time loss injury. Furthermore, prolonged series of match play and increased in-season injury reduced the probability of not sustaining an injury. For predictor selection, the Elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time loss injuries have been identified appropriate methods for improving real life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.</p

Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study

BACKGROUND: Most adults presenting in primary care with chest pain symptoms will not receive a diagnosis ("unattributed" chest pain) but are at increased risk of cardiovascular events. AIM: To assess within patients with unattributed chest pain, risk factors for cardiovascular events and whether those at greatest risk of cardiovascular disease can be ascertained by an existing general population risk prediction model or by development of a new model. METHODS: The study used UK primary care electronic health records from the Clinical Practice Research Datalink (CPRD) linked to admitted hospitalisations. Study population was patients aged 18 plus with recorded unattributed chest pain 2002-2018. Cardiovascular risk prediction models were developed with external validation and comparison of performance to QRISK3, a general population risk prediction model. RESULTS: There were 374,917 patients with unattributed chest pain in the development dataset. Strongest risk factors for cardiovascular disease included diabetes, atrial fibrillation, and hypertension. Risk was increased in males, patients of Asian ethnicity, those in more deprived areas, obese patients, and smokers. The final developed model had good predictive performance (external validation c-statistic 0.81, calibration slope 1.02). A model using a subset of key risk factors for cardiovascular disease gave nearly identical performance. QRISK3 underestimated cardiovascular risk. CONCLUSION: Patients presenting with unattributed chest pain are at increased risk of cardiovascular events. It is feasible to accurately estimate individual risk using routinely recorded information in the primary care record, focusing on a small number of risk factors. Patients at highest risk could be targeted for preventative measures

Abstract Vision-Based Urban Navigation Procedures for Verbally Instructed Robots

When humans explain a task to be executed by a robot they decompose it into chunks of actions. These form a chain of search-and-act sensory-motor loops that exit when a condition is met. In this paper we investigate the nature of these chunks in an urban visual navigation context, and propose a method for implementing the corresponding robot primitives such as &amp;quot;take the n th turn right/left&amp;quot;. These primitives make use of a &amp;quot;shortlived&amp;quot; internal map updated as the robot moves along. The recognition and localisation of intersections is done in the map using task-guided template matching. This approach takes advantage of the content of human instructions to save computation time and improve robustness