Sports Injury Prevention Screen (SIPS): Design and Architecture of an Internet of Things (IoT) Based Analytics Health App

At present, technology-based injury risk screening methods are typically utilized by large and well-funded athletic programs at both the professional and collegiate levels. Such screening is not available to athletes who participate in most scholastic and amateur programs, due to the high cost of testing equipment and the need for oversight by medical professionals who possess the appropriate level of expertise. However, a mobile health app for injury risk screening can eliminate these obstacles, thereby facilitating the availability of systematic injury prevention initiatives to a much larger population of athletes. This study describes the design and architecture of a mobile health app for Sports Injury Prevention Screening (SIPS). SIPS enables athletic programs with limited funding and personnel the ability to conduct individualized injury risk assessments and deploy personalized injury prevention plans that are currently available only at collegiate and professional levels. Even for well funded athletic programs, typical injury screening methods are restricted to assessing either an athlete’s musculoskeletal coordination or neurocognitive abilities; assessing both simultaneously is only possible in the laboratory environment. SIPS introduces a novel, dual-task assessment by using two devices simultaneously to measure an athlete’s neuro-mechanical responsiveness without the requirements of a laboratory and expert-level domain knowledge. Single device tests, designed specifically to replicate established injury screening techniques using just a mobile phone, are also available in SIPS. Data is collected for all of these tests from devices’ motion sensors and is synchronized using Bluetooth® technology. Ongoing work is integrating various predictive analytics algorithms for providing real time feedback to the athlete, medical director and coaches

Hardware Acceleration of Progressive Refinement Radiosity using Nvidia RTX

A vital component of photo-realistic image synthesis is the simulation of indirect diffuse reflections, which still remain a quintessential hurdle that modern rendering engines struggle to overcome. Real-time applications typically pre-generate diffuse lighting information offline using radiosity to avoid performing costly computations at run-time. In this thesis we present a variant of progressive refinement radiosity that utilizes Nvidia's novel RTX technology to accelerate the process of form-factor computation without compromising on visual fidelity. Through a modern implementation built on DirectX 12 we demonstrate that offloading radiosity's visibility component to RT cores significantly improves the lightmap generation process and potentially propels it into the domain of real-time.Comment: 114 page

Space programs summary no. 37-64, volume 1 for the period 1 May to 30 June 1970. Flight projects

Mariner and Viking flight projects for Mars exploratio

Development of biomechanical-based analysis tools for the evaluation of infringements and performance in race-walking

The study aims at developing an innovative biomechanical based methodology for the performance and infringements assessment in race-walking using a wearable inertial system

Improved micro-contact resistance model that considers material deformation, electron transport and thin film characteristics

This paper reports on an improved analytic model forpredicting micro-contact resistance needed for designing microelectro-mechanical systems (MEMS) switches. The originalmodel had two primary considerations: 1) contact materialdeformation (i.e. elastic, plastic, or elastic-plastic) and 2) effectivecontact area radius. The model also assumed that individual aspotswere close together and that their interactions weredependent on each other which led to using the single effective aspotcontact area model. This single effective area model wasused to determine specific electron transport regions (i.e. ballistic,quasi-ballistic, or diffusive) by comparing the effective radius andthe mean free path of an electron. Using this model required thatmicro-switch contact materials be deposited, during devicefabrication, with processes ensuring low surface roughness values(i.e. sputtered films). Sputtered thin film electric contacts,however, do not behave like bulk materials and the effects of thinfilm contacts and spreading resistance must be considered. Theimproved micro-contact resistance model accounts for the twoprimary considerations above, as well as, using thin film,sputtered, electric contact

A feasibility study on pairing a smartwatch and a mobile device through multi-modal gestures

Pairing is the process of establishing an association between two personal devices. Although such a process is intuitively very simple, achieving a straightforward and secure association is challenging due to several possible attacks and usability-related issues. Indeed, malicious attackers might want to spoof the communication between devices in order to gather sensitive information or harm them. Moreover, offering users simple and usable schemes which attain a high level of security remains a major issue. In addition, due to the great diversity of pairing scenarios and equipment, achieving a single, usable, secure association for all possible devices and use cases is simply not possible. In this thesis, we study the feasibility of a novel pairing scheme based on multi-modal gestures, namely, gestures involving drawing supported by accelerometer data. In particular, a user can pair a smart-watch on his wrist and a mobile device (e.g., a smart-phone) by simply drawing with a finger on the screen at the device. To this purpose, we developed mobile applications for smart-watch and smart-phone to sample and process sensed data in support of a secure commitment-based protocol. Furthermore, we performed experiments to verify whether encoded matching-movements have a clear similarity compared to non-matching movements. The results proved that it is feasible to implement such a scheme which also offers users a natural way to perform secure pairing. This innovative scheme may be adopted by a large number of mobile devices (e.g., smart-watches, smart-phones, tablets, etc.) in different scenarios

Detecting head movement using gyroscope data collected via in-ear wearables

Abstract. Head movement is considered as an effective, natural, and simple method to determine the pointing towards an object. Head movement detection technology has significant potentiality in diverse field of applications and studies in this field verify such claim. The application includes fields like users interaction with computers, controlling many devices externally, power wheelchair operation, detecting drivers’ drowsiness while they drive, video surveillance system, and many more. Due to the diversity in application, the method of detecting head movement is also wide-ranging. A number of approaches such as acoustic-based, video-based, computer-vision based, inertial sensor data based head movement detection methods have been introduced by researchers over the years. In order to generate inertial sensor data, various types of wearables are available for example wrist band, smart watch, head-mounted device, and so on. For this thesis, eSense — a representative earable device — that has built-in inertial sensor to generate gyroscope data is employed. This eSense device is a True Wireless Stereo (TWS) earbud. It is augmented with some key equipment such as a 6-axis inertial motion unit, a microphone, and dual mode Bluetooth (Bluetooth Classic and Bluetooth Low Energy). Features are extracted from gyroscope data collected via eSense device. Subsequently, four machine learning models — Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes, and Perceptron — are applied aiming to detect head movement. The performance of these models is evaluated by four different evaluation metrics such as Accuracy, Precision, Recall, and F1 score. Result shows that machine learning models that have been applied in this thesis are able to detect head movement. Comparing the performance of all these machine learning models, Random Forest performs better than others, it is able to detect head movement with approximately 77% accuracy. The accuracy rate of other three models such as Support Vector Machine, Naïve Bayes, and Perceptron is close to each other, where these models detect head movement with about 42%, 40%, and 39% accuracy, respectively. Besides, the result of other evaluation metrics like Precision, Recall, and F1 score verifies that using these machine learning models, different head direction such as left, right, or straight can be detected

Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

A study on virtual reality and developing the experience in a gaming simulation

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Masters by ResearchVirtual Reality (VR) is an experience where a person is provided with the freedom of viewing and moving in a virtual world [1]. The experience is not constrained to a limited control. Here, it was triggered interactively according to the user’s physical movement [1] [2]. So the user feels as if they are seeing the real world; also, 3D technologies allow the viewer to experience the volume of the object and its prospection in the virtual world [1]. The human brain generates the depth when each eye receives the images in its point of view. For learning for and developing the project using the university’s facilities, some of the core parts of the research have been accomplished, such as designing the VR motion controller and VR HMD (Head Mount Display), using an open source microcontroller. The VR HMD with the VR controller gives an immersive feel and a complete VR system [2]. The motive was to demonstrate a working model to create a VR experience on a mobile platform. Particularly, the VR system uses a micro electro-mechanical system to track motion without a tracking camera. The VR experience has also been developed in a gaming simulation. To produce this, Maya, Unity, Motion Analysis System, MotionBuilder, Arduino and programming have been used. The lessons and codes taken or improvised from [33] [44] [25] and [45] have been studied and implemented