Using Motion Controllers in Virtual Conferencing

At the end of 2010 Microsoft released a new controller for the Xbox 360 called Kinect. Unlike ordinary video game controllers, the Kinect works by detecting the positions and movements of a user’s entire body using the data from a sophisticated camera that is able to detect the distance between itself and each of the points on the image it is capturing. The Kinect device is essentially a low-cost, widely available motion capture system. Because of this, almost immediately many individuals put the device to use in a wide variety applications beyond video games. This thesis investigates one such use; specifically the area of virtual meetings. Virtual meetings are a means of holding a meeting between multiple individuals in multiple locations using the internet, akin to teleconferencing or video conferencing. The defining factor of virtual meetings is that they take place in a virtual world rendered with 3D graphics; with each participant in a meeting controlling a virtual representation of them self called an avatar. Previous research into virtual reality in general has shown that there is the potential for people to feel highly immersed in virtual reality, experiencing a feeling of really ‘being there’. However, previous work looking at virtual meetings has found that existing interfaces for users to interact with virtual meeting software can interfere with this experience of ‘being there’. The same research has also identified other short comings with existing virtual meeting solutions. This thesis investigates how the Kinect device can be used to overcome the limitations of exiting virtual meeting software and interfaces. It includes a detailed description of the design and development of a piece of software that was created to demonstrate the possible uses of the Kinect in this area. It also includes discussion of the results of real world testing using that software, evaluating the usefulness of the Kinect when applied to virtual meetings

The use of consumer depth cameras for calculating body segment parameters.

Body segment parameters (BSPs) are pivotal to a number of key analyses within sports and healthcare. Accuracy is paramount, as investigations have shown small errors in BSPs to have significant impact upon subsequent analyses, particularly when analysing the dynamics of high acceleration movements. There are many techniques with which to estimate BSPs, however, the majority are complex, time consuming, and make large assumptions about the underlying structure of the human body, leading to considerable errors. Interest is increasingly turning towards obtaining person-specific BSPs from 3D scans, however, the majority of current scanning systems are expensive, complex, require skilled operators, and require lengthy post processing of the captured data. The purpose of this study was to develop a low cost 3D scanning system capable of estimating accurate and reliable person-specific segmental volume, forming a fundamental first step towards calculation of the full range of BSPs.A low cost 3D scanning system was developed, comprising four Microsoft Kinect RGB-D sensors, and capable of estimating person-specific segmental volume in a scanning operation taking less than one second. Individual sensors were calibrated prior to first use, overcoming inherent distortion of the 3D data. Scans from each of the sensors were aligned with one another via an initial extrinsic calibration process, producing 360° colour rendered 3D scans. A scanning protocol was developed, designed to limit movement due to postural sway and breathing throughout the scanning operation. Scans were post processed to remove discontinuities at edges, and parameters of interest calculated using a combination of manual digitisation and automated algorithms.The scanning system was validated using a series of geometric objects representative of human body segments, showing high reliability and systematic over estimation of scan-derived measurements. Scan-derived volumes of living human participants were also compared to those calculated using a typical geometric BSP model. Results showed close agreement, however, absolute differences could not be quantified owing to the lack of gold standard data. The study suggests the scanning system would be well received by practitioners, offering many advantages over current techniques. However, future work is required to further characterise the scanning system's absolute accuracy

A machine learning approach for clinical gait analysis and classification of polymyalgia rheumatica using myoelectric sensors

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe study focuses on Polymyalgia Rheumatica (PMR), an autoimmune musculoskeletal disease primarily affecting the shoulder blade and hip muscles in older adults, particularly women aged 50 and above. The research aims to address two main challenges: the need for more clarity on the disease's pathophysiology and the challenge of identifying disease severity in patients. The study introduces a novel approach involving movement assessment, by designing a low-cost MyoTracker system, and using electromyography (EMG) features to understand the impact on patients' hip muscles. A clinical trial was conducted at Komfo Anokye Teaching Hospital in Ghana, where the study employed a qualitative research approach to monitor movement patterns. Participants were tasked to perform exercises comprising of gait, knee lifting, and knee extension with sensors attached to the hip muscles. This research unfolds in three iterations, the first investigation involved hip muscular imbalances where the significant difference between patients and healthy controls in the maximum voluntary contraction (MVC) values was recorded. The bilateral difference computed between the left and right hip in patients exhibited 15% MVC on average compared to the healthy control group's 6%, indicating substantial hip muscular imbalances. The second iteration involved a movement assessment to identify specific movement patterns in patients. Support Vector Machine (SVM) achieves 85% accuracy for gait exercises, while Decision Tree (DT) performs less efficiently at 70%. SVM also excels in knee lifting exercises (70% accuracy), outperforming DT (60%). Based on hip muscle activation, patients' movement patterns significantly differ from healthy controls. In the third iteration, deep learning techniques, specifically RNN-LSTM and Vision Transformer (ViT), classify PMR disease severity based on EMG features. The study's results carry significant clinical implications with the evidence of hip muscular imbalances aiding in designing tailored rehabilitation protocols. Importantly, this study uses a cost-effective method for determining disease severity, enabling predictions about patients with severe PMR conditions. The key contribution of this thesis is the identification of patients’ specific movement patterns and the determination of PMR severity among patients. Other contributions are the detection of hip muscular imbalance in patients and the design of rehabilitation protocols to address hip muscular imbalances and improve patients' range of motion, enhancing overall well-being. In conclusion, this comprehensive study leverages innovative approaches, from a MyoTracker system for movement assessment to deep learning models, to unravel the complexities of PMR disease. The collaboration with medical experts emphasises the potential real-world impact of this research in enhancing the treatment and recovery processes for individuals.Ghana Scholarship Secretaria

Adventures in software engineering : plugging HCI & acessibility gaps with open source solutions

There has been a great deal of research undertaken in the field of Human-Computer Interfaces (HCI), input devices, and output modalities in recent years. From touch-based and voice control input mechanisms such as those found on modern smart-devices to the use of touch-free input through video-stream/image analysis (including depth streams and skeletal mapping) and the inclusion of gaze tracking, head tracking, virtual reality and beyond - the availability and variety of these I/O (Input/Output) mechanisms has increased tremendously and progressed both into our living rooms and into our lives in general. With regard to modern desktop computers and videogame consoles, at present many of these technologies are at a relatively immature stage of development - their use often limited to simple adjuncts to the staple input mechanisms of mouse, keyboard, or joystick / joypad inputs. In effect, we have these new input devices - but we're not quite sure how best to use them yet; that is, where their various strengths and weaknesses lie, and how or if they can be used to conveniently and reliably drive or augment applications in our everyday lives. In addition, much of this technology is provided by proprietary hardware and software, providing limited options for customisation or adaptation to better meet the needs of specific users. Therefore, this project investigated the development of open source software solutions to address various aspects of innovative user I/O in a flexible manner. Towards this end, a number of original software applications have been developed which incorporate functionality aimed at enhancing the current state of the art in these areas and making that software freely available for use by any who may find it beneficial.Doctor of Philosoph

Improving the Security of Mobile Devices Through Multi-Dimensional and Analog Authentication

Mobile devices are ubiquitous in today\u27s society, and the usage of these devices for secure tasks like corporate email, banking, and stock trading grows by the day. The first, and often only, defense against attackers who get physical access to the device is the lock screen: the authentication task required to gain access to the device. To date mobile devices have languished under insecure authentication scheme offerings like PINs, Pattern Unlock, and biometrics-- or slow offerings like alphanumeric passwords. This work addresses the design and creation of five proof-of-concept authentication schemes that seek to increase the security of mobile authentication without compromising memorability or usability. These proof-of-concept schemes demonstrate the concept of Multi-Dimensional Authentication, a method of using data from unrelated dimensions of information, and the concept of Analog Authentication, a method utilizing continuous rather than discrete information. Security analysis will show that these schemes can be designed to exceed the security strength of alphanumeric passwords, resist shoulder-surfing in all but the worst-case scenarios, and offer significantly fewer hotspots than existing approaches. Usability analysis, including data collected from user studies in each of the five schemes, will show promising results for entry times, in some cases on-par with existing PIN or Pattern Unlock approaches, and comparable qualitative ratings with existing approaches. Memorability results will demonstrate that the psychological advantages utilized by these schemes can lead to real-world improvements in recall, in some instances leading to near-perfect recall after two weeks, significantly exceeding the recall rates of similarly secure alphanumeric passwords