Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

BioMeT and algorithm challenges: A proposed digital standardized evaluation framework

Technology is advancing at an extraordinary rate. Continuous flows of novel data are being generated with the potential to revolutionize how we better identify, treat, manage, and prevent disease across therapeutic areas. However, lack of security of confidence in digital health technologies is hampering adoption, particularly for biometric monitoring technologies (BioMeTs) where frontline healthcare professionals are struggling to determine which BioMeTs are fit-for-purpose and in which context. Here, we discuss the challenges to adoption and offer pragmatic guidance regarding BioMeTs, cumulating in a proposed framework to advance their development and deployment in healthcare, health research, and health promotion. Furthermore, the framework proposes a process to establish an audit trail of BioMeTs (hardware and algorithms), to instill trust amongst multidisciplinary users

Personalized ambient parameters monitoring: design and implementing of a wrist-worn prototype for hazardous gases and sound level detection

The concentration is on “3D space utilization” as the concept and infrastructure of designing of a wearable in ambient parameters monitoring. This strategy is implemented according to “multi-layer” approach. In this approach, each group of parameters from the same category is monitored by a modular physical layer enriched with the respected sensors. Depending on the number of parameters and layers, each physical layer is located on top of another. The intention is to implement a device for “everyone in everywhere for everything”

Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment

Privacy in characterizing and recruiting patients for IoHT-aided digital clinical trials

Nowadays there is a tremendous amount of smart and connected devices that produce data. The so-called IoT is so pervasive that its devices (in particular the ones that we take with us during all the day - wearables, smartphones...) often provide some insights on our lives to third parties. People habitually exchange some of their private data in order to obtain services, discounts and advantages. Sharing personal data is commonly accepted in contexts like social networks but individuals suddenly become more than concerned if a third party is interested in accessing personal health data. The healthcare systems worldwide, however, begun to take advantage of the data produced by eHealth solutions. It is clear that while on one hand the technology proved to be a great ally in the modern medicine and can lead to notable benefits, on the other hand these processes pose serious threats to our privacy. The process of testing, validating and putting on the market a new drug or medical treatment is called clinical trial. These trials are deeply impacted by the technological advancements and greatly benefit from the use of eHealth solutions. The clinical research institutes are the entities in charge of leading the trials and need to access as much health data of the patients as possible. However, at any phase of a clinical trial, the personal information of the participants should be preserved and maintained private as long as possible. During this thesis, we will introduce an architecture that protects the privacy of personal data during the first phases of digital clinical trials (namely the characterization phase and the recruiting phase), allowing potential participants to freely join trials without disclosing their personal health information without a proper reward and/or prior agreement. We will illustrate what is the trusted environment that is the most used approach in eHealth and, later, we will dig into the untrusted environment where the concept of privacy is more challenging to protect while maintaining usability of data. Our architecture maintains the individuals in full control over the flow of their personal health data. Moreover, the architecture allows the clinical research institutes to characterize the population of potentiant users without direct access to their personal data. We validated our architecture with a proof of concept that includes all the involved entities from the low level hardware up to the end application. We designed and realized the hardware capable of sensing, processing and transmitting personal health data in a privacy preserving fashion that requires little to none maintenance