A wearable robot should know its environment and its location in order to help its operator. Wearable robots are becoming more feasible with the development of more powerful and smaller computing devices and cameras. The main aim of this research is to build a wearable robot with a low cost stereo camera system which explores a room sized unknown environment online and automatically. To achieve 3D localization and map building for the wearable robot, a consistent visual-SLAM algorithm is implemented by using point features in the environment and Extended Kalman Filter for state estimation. The whole system includes camera models and calibration, feature extraction, depth measurement and Extended Kalman Filter algorithm. Moreover, a map management algorithm is developed. This algorithm keeps the number of features spatially uniform in the scene and adds new features when feature number decreases in a frame. Furthermore, a user-interface is presented so that the location of the camera,the features and the constructed map are visualized online. Most importantly, the system is conducted by a low-cost stereo system.

Due to the rapid development in electronics and radio communication systems, modern technologies are implemented to improve the safety and security of workplaces in order to save field service engineers lives and their health. In this thesis, an automated safety suit was implemented with integrated sensors for monitoring the safety of field service engineers. The basic idea of the prototype is to ensure safety for the field service engineers who are working in adverse environmental conditions. This safety suit includes embedded devices which can communicate with mobile devices and by means of that provides aid for the people working in several fields such as confined spaces, high altitudes etc. In this prototype, a different type of sensors are proposed for monitoring environmental and health conditions like temperature, CO gas levels, relative humidity, body temperature and heartbeat. A mobile application is proposed to monitor and control the automated safety suit, which also identifies the environmental changes and provide prompt alerts to the user. Keeping the usage of automated safety suit in mind, the system is designed in a user friendly manner and all the key elements are considered and implemented accordingly for the requirements of service engineers who are working in confined spaces and hazardous places.

This thesis describes the design and development of the SmartBadge; an electronic replacement for the standard paper name badge worn at conferences and similar events. Both hardware and software have been designed for the SmartBadge; the hardware has been developed around a CC1010 microcontroller and RF transceiver. Attached to this are an infrared transceiver, an LCD display, some LEDs, buttons and a piezoelectric buzzer. There is also an antenna for the RF transceiver whose design is the result of SuperNEC [1] simulations. Protocol software development has focussed on the communication between a SmartBadge and other badges and base stations, yet there is still space available in the CC1010s flash memory to develop applications beyond the business card exchange example developed to demonstrate the communication software. The SmartBadge communicates with other badges by using the infrared transceiver. In the business card application a SmartBadge is worn by a person and is collecting the ID and a time counter from SmartBadges worn by other facing people as this person mingles through a conference or similar event. This data is then collected in real time using the RF transceiver to communicate with base stations which would be scattered around the venue. The RF network has been designed as a single hop network and a new Medium Access Control (MAC) protocol has been designed to allow the SmartBadges to share the links to the base stations while conserving as much energy as possible. This protocol is called Uplink MAC (or U-MAC) and is described in section 6.2.

Tremendous advances in wearable computing and storage technologies enable us to record not just snapshots of an event but the whole human experience for a long period of time. Such a \life-logandamp;quot; system captures important events as they happen, rather than an after-thought. Such a system has applications in many areas such as law enforcement, personal archives, police questioning, and medicine. Much of the existing eandamp;reg;orts focus on the pattern recognition and information retrieval aspects of the system. On the other hand, the privacy issues raised by such an intrusive system have not received much attention from the research community. The objectives of this research project are two-fold: andamp;macr;rst, to construct a wearable life-log video system, and second, to provide a solution for protecting the identity of the subjects in the video while keeping the video useful. In this thesis work, we designed a portable wearable life-log system that implements audio distortion and face blocking in a real time to protect the privacy of the subjects who are being recorded in life-log video. For audio, our system automatically isolates the subject's speech and distorts it using a pitch- shifting algorithm to conceal the identity. For video, our system uses a real-time face detection, tracking and blocking algorithm to obfuscate the faces of the subjects. Extensive experiments have been conducted on interview videos to demonstrate the ability of our system in protecting the identity of the subject while maintaining the usability of the life-log video.

A Wearable Mobility Monitoring System (WMMS) can be an advantageous device for rehabilitation decision-making. This thesis presents the design and evaluation of a proof-of-concept WMMS that uses the BlackBerry Smartphone platform. A Java program was developed for the BlackBerry 9550, using the integrated tri-axial accelerometer, Global Positioning System sensor (GPS), CMOS digital video camera, and timer to identify change-of-state (CoS) among static states, dynamic states, small activity of daily living (ADL) movements, and car riding. Static states included sitting, lying, standing, and taking an elevator. Dynamic states included walking on level ground, walking on stairs, and walking on a ramp. Small activity of daily living movements included bathroom activities, working in the kitchen, and meal preparation. Following feature extraction from the sensor data, two decision trees were used to distinguish CoS and mobility activities. CoS identification subsequently triggered video recording for improved mobility context analysis during post-processing.

Research to improve artificial renal replacement therapies is varied across the many different parts of a hemodialysis system. Work largely focuses on developing a better dialyzer - the component that is directly responsible for removing wastes from the blood - but less study is devoted to the entire hemodialysis system. This work seeks to improve hemodialysis in two ways: by proposing a new renal replacement therapy that does not rely on traditional hemodialysis components, and by investigating the feasibility of adapting current hemodialysis practices to a portable format. While an alternative renal replacement therapy may be the best solution to today's dialysis problems, this work further focuses on reducing hemodialysis to a portable format through systematic engineering design. In that process, a detailed system model is made in Simulink that can account for the large number of inputs of such a system - the blood flow rate, dialyzer size, treatment time, etc. - allowing for detailed exploration of the design space. Once the model is completed, it is verified through in vitro experiments carried out with porcine blood. Additionally, the model is verified against published human hemodialysis data. After model verification, hemodialysis concepts are generated that allow for maximum portability under different patient conditions.

Thesis (M.F.A.)--Rochester Institute of Technology, 1994. / Typescript. Includes bibliographical references (leaf 18).

Thesis (Ph. D.)--Ohio State University, 2007. / Full text release at OhioLINK's ETD Center delayed at author's request

Background Wearable technology, which is a part of the Internet of Things (IoT), appears to be an upcoming trend with increasing importance within the business world. Nevertheless, no clear business model for companies working with wearables had been defined yet taking the influences wearables have on businesses and especially their value proposition into consideration. Purpose The purpose of this thesis is to offer input to the lack of existing literature within business models and wearables technology. The aim is to unfold a general business model that can be used within wearable companies/IoT businesses and show the influence these technologies have on them. Methodology In order to conduct an empirical research a multiple case study has been conducted, based on semi-structured interviews with eight companies, which core business consists out of wearable technology. The frameworks on business models by Gassmann et al (2014) and Osterwalder and Pigneur (2010) serve as the basis for this study and its analysis, which is based on a grounded theory approach. Results It appears that a great amount of similarities can be found through the cross-case analysis between the cases. This makes the construction of a new business model possible. The unfolded model gives also a new contribution to the theory of Hui (2014) regarding a new area of value creation and value capture within IoT businesses.

BACKGROUND. Unhealthy lifestyle behaviours such as physical inactivity are global risk factors for chronic disease. Despite this, a substantial proportion of the UK population fail to achieve the recommended levels of physical activity. This may partly be because the health messages presently disseminated are not sufficiently potent to evoke behaviour change. There has been an exponential growth in the availability of digital health technologies within the consumer marketplace. This influx of technology has allowed people to self-monitor a plethora of health indices, such as their physical activity, in real-time. However, changing movement behaviours is difficult and often predicated on the assumption that individuals are willing to change their lifestyles today to reduce the risk of developing disease years or even decades later. One approach that may help overcome this challenge is to present physiological feedback in parallel with physical activity feedback. In combination, this approach may help people to observe the acute health benefits of being more physically active and subsequently translate that insight into a more physically active lifestyle. AIMS. Study One aimed to review existing studies employing fMRI to examine neurological responses to health messages pertaining to physical activity, sedentary behaviour, smoking, diet and alcohol consumption to assess the capacity for fMRI to assist in evaluating health behaviours. Study Two aimed to use fMRI to evaluate physical activity, sedentary behaviour and glucose feedback obtained through wearable digital health technologies and to explore associations between activated brain regions and subsequent changes in behaviour. Study Three aimed to explore engagement of people at risk of type 2 diabetes using digital health technologies to monitor physical activity and glucose levels. METHODS. Study One was a systematic review of published studies investigating health messages relating to physical activity, sedentary behaviour, diet, smoking or alcohol consumption using fMRI. Study Two asked adults aged 30-60 years to undergo fMRI whilst presented personalised feedback on their physical activity, sedentary behaviour and glucose levels, following a 14-day wear protocol of an accelerometer, inclinometer and flash glucose monitor. Study Three was a six-week, three-armed randomised feasibility trial for individuals at moderate-to-high risk of developing type 2 diabetes. The study used commercially available wearable physical activity (Fitbit Charge 2) and flash glucose (Freestyle Libre) technologies. Group 1 were offered glucose feedback for 4 weeks followed by glucose plus physical activity feedback for 2 weeks (G4GPA2). Group 2 were offered physical activity feedback for 4 weeks followed by glucose plus physical activity feedback for 2 weeks (PA4GPA2). Group 3 were offered glucose plus physical activity feedback for six weeks (GPA6). The primary outcome for the study was engagement, measured objectively by time spent on the Fitbit app, LibreLink app (companion app for the Freestyle Libre) as well as the frequency of scanning the Freestyle Libre and syncing the Fitbit. RESULTS. For Study One, 18 studies were included in the systematic review and of those, 15 examined neurological responses to smoking related health messages. The remaining three studies examined health messages about diet (k=2) and physical activity (k=1). Areas of the prefrontal cortex and amygdala were most commonly activated with increased activation of the ventromedial prefrontal cortex predicting subsequent behaviour (e.g. smoking cessation). Study Two identified that presenting people with personalised feedback relating to interstitial glucose levels resulted in significantly more brain activation when compared with feedback on personalised movement behaviours (P < .001). Activations within regions of the prefrontal cortex were significantly greater for glucose feedback compared with feedback on personalised movement behaviours. Activation in the subgyral area was correlated with moderate-to-vigorous physical activity at follow-up (r=.392, P=.043). In Study Three, time spent on the LibreLink app significantly reduced for G4GPA2 and GPA6 (week 1: 20.2±20 versus week 6: 9.4±14.6min/day, p=.007) and significantly fewer glucose scans were recorded (week 1: 9.2±5.1 versus week 6: 5.9±3.4 scans/day, p=.016). Similarly, Fitbit app usage significantly reduced (week 1: 7.1±3.8 versus week 6: 3.8±2.9min/day p=.003). The number of Fitbit syncs did not change significantly (week 1: 6.9±7.8 versus week 6: 6.5±10.2 syncs/day, p=.752). CONCLUSIONS. Study One highlighted the fact that thus far the field has focused on examining neurological responses to health messages using fMRI for smoking with important knowledge gaps in the neurological evaluation of health messages for other lifestyle behaviours. The prefrontal cortex and amygdala were most commonly activated in response to health messages. Using fMRI, Study Two was able to contribute to the knowledge gaps identified in Study One, with personalised glucose feedback resulting in a greater neurological response than personalised feedback on physical activity and sedentary behaviour. From this, Study Three found that individuals at risk of developing type 2 diabetes were able to engage with digital health technologies offering real-time feedback on behaviour and physiology, with engagement diminishing over time. Overall, this thesis demonstrates the potential for digital health technologies to play a key role in feedback paradigms relating to chronic disease prevention.