This thesis presents the design and implementation of I-Shop, a context-aware, shopping smartphone application designed to provide shoppers with relevant advertisements for product and services available in close proximity. We argue that current context-aware mobile applications exhibit significant limitations in the following domains: (1) use of context, (2) invasion of privacy, (3) spam management, and (4) platform dependency. The proposed context model attempts to tackle these shortcomings by exploiting available contextual information from social media networks such as Facebook. Our goal is to use a user’s personal information, such as their native language and personal interests, to direct the most relevant advertisements to them. To alleviate any privacy issues, a user’s personal information is never sent out to any back-end services and only apply the filters locally. In addition, unlike most other predictive approaches that track the user’s location history, we follow a reactive approach which triggers only when the user is close to a shopping area. When a user arrives to a particular shopping area, the application asks whether she wishes to view any advertisements of local products and services. Upon approval, the application retrieves deals on products including services sorted by domain from databases, such as Groupon and our custom extended deals database. Finally, the application filters the retrieved data according to personal interests and then displays the results. As a proof of concept, we designed and implemented the I-Shop prototype application. We built I-Shop as a hybrid application using IBM’s state-of-the-art Worklight infrastructure. This approach lets developers optimize their time and effort; enabling a “write once, deploy everywhere” development model that not only reduces development costs but also increases application performance by providing a combination of native and web capabilities. In addition, I-Shop also leverages several features offered by the IBM Worklight infrastructure including cross-platform support, direct update, internalization, and integration of third-party libraries and toolkits. / Graduate

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / Falls prevention efforts for older adults have become increasingly important and are now a significant research effort. As part of the prevention effort, analysis of gait has become increasingly important. Data is typically collected in a laboratory setting using 3-D motion capture, which can be time consuming, invasive and requires expensive and specialized equipment as well as trained operators. Inertial sensors, which are smaller and more cost effective, have been shown to be useful in falls research. Smartphones now contain Micro Electro-Mechanical (MEM) Inertial Measurement Units (IMUs), which make them a compelling platform for gait data acquisition. This paper reports the development of an iOS app for collecting accelerometer data and an offline machine learning system to classify a subject, based on this data, as faller or non-faller based on their history of falls. The system uses the accelerometer data captured on the smartphone, extracts discriminating features, and then classifies the subject based on the feature vector. Through simulation, our preliminary and limited study suggests this system has an accuracy as high as 85%. Such a system could be used to monitor an at-risk person's gait in order to predict an increased risk of falling.

As the population ages, prediction of falls risk is becoming an increasingly important research area. Due to built-in inertial sensors and ubiquity, smartphones provide an at- tractive data collection and computing platform for falls risk prediction and continuous gait monitoring. One challenge in continuous gait monitoring is that signi cant signal variability exists between individuals with a high falls risk and those with low-risk. This variability increases the di cultly in building a universal system which segments and labels changes in signal state. This paper presents a method which uses unsu- pervised learning techniques to automatically segment a gait signal by computing the dissimilarity between two consecutive windows of data, applying an adaptive threshold algorithm to detect changes in signal state, and using a rule-based gait recognition al- gorithm to label the data. Using inertial data,the segmentation algorithm is compared against manually segmented data and is capable of achieving recognition rates greater than 71.8%.

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The powerful technical capabilities of smartphones offer unprecedented opportunities for collecting dietary information. We have developed an enhanced smartphone application called MyEnergyBalance, which permits imaged-based self-monitoring of all foods consumed, and links to a convenient and user-friendly web-based dietary assessment tool. The primary objective of this pilot study was to determine if the MyEnergyBalance app (with use of images) in combination of the associated website improves dietary recall compared to diet analysis on the MyEnergyBalance website alone. We also generated preliminary data on the usability of the MyEnergyBalance iPhone app and website. This pilot study was a crossover study design of healthy, college students. Participants were randomly assigned to two groups. Both groups consumed their normal diet for the first day with one group recording their food intake with image functions of the MyEnergyBalance app, while the other group did not use the app. On the second day, all participants logged into the MyEnergyBalance website to record their food intake from the previous day; one group using the images from the app to assist in recalling what they ate, while the other group recalled what they ate from memory. The diet analysis results were compared to those obtained using the ASA24 website. The groups were then crossed over to the opposite vs no-image assisted recalls. Ten participants (seven females and three males) aged 20 to 22 years completed this study. The average BMI of all participants was 23.12 kg/m2 (ranging from 18.95 to 32.28 kg/m2). There was no statistically significant differences in the estimates of the energy intake between the MyEnergyBalance app and website compared to ASA24. The SUS mean score for the MyEnergyBalance app and website was 86 and 69.5, respectively. A strong, negative correlation was found between the system usability scale scores and the absolute differences in energy intake of the MyEnergyBalance app and ASA24. Although we were not able to demonstrate a significant benefit of the images from the iPhone app at improving food recall (perhaps due to the small study sample size), we were able to demonstrate a high usability score for the iPhone app, average usability score for the website, and a significant correlation between subjects' usability scores and relative accuracy of the subjects' food recall using the images from the iPhone app. A future study with a larger sample size will hopefully provide more information on the efficacy of image-based food recalls.

The chapter house of the Benedictine priory of Saint John Le Bas-Nueil, currently located in the Worcester Art Museum, is an impressive piece of architecture. However, visitors are currently restricted to admiring the structure and its restoration only, as there is limited information presented in the museum about the room’s original use. The purpose of this project was to produce a low-impact, narrative-driven audio experience designed to increase visitor interest in the museum in general and Benedictine life during the twelfth century in particular. The prototype produced combines elements of traditional audio tours, radio drama, and question-and-answer interaction sequences to provide a self-driven immersive experience.

The quantity of cell phone applications or mobile apps have seen an upsurge at an exponential rate in under a decade. Many have been created for a variety of industries, including transportation. The advent and subsequent commercialized implementation of near-instant transport by a middleman-type of app is now known as a Transportation Network Company or TNC. Examples of the more renowned TNCs are Uber, Lyft and Sidecar. In recent years, TNCs have cultivated a tremendous following, to the degree of taxicab desertion. Moreover, the massive success of TNCs led to expansion of its capacities into public transportation. The TNC’s expeditious popularity has garnered the attention of government and transit agencies. Without fail, TNCs can complement, supplement or compete with transit. However, sparsely has there been any deep discussion about a TNC potentially supplanting transit. The aim of this paper is to show how TNCs could replace public transportation in the United States if subsidized at the same level of transit agencies. Austin, Texas was analyzed as the case study city. A comparison of subsidization between Austin’s transit agency: Cap Metro, the local TNCs, and on a national aggregate level was conducted. The evidence herein clearly shows that TNCs are highly competitive when in revenue service operating at full capacity, potentially replacing public transportation.

The demand for control systems that are highly capable of driving solid-state optoelectronic devices has significantly increased with the advancement of their efficiency and elevation of their current consumption. This work presents a closed-loop control system that is based on a microcontroller embedded system capable of driving high power optoelectronic devices. In this version of the system, the device in the center of control is a high-power red, green, and blue light emitting diode package. The system features a graphical user interface, namely an Android mobile phone application, in which the user can easily use to vary the light color and intensity of the light-emitting device wirelessly via Bluetooth. Included in the system is a feedback mechanism constituted by a red, green, and blue color sensor through which the user can use to observe feedback color information about the emitted light. The system has many commercial application including in-door lighting and research application including plant agriculture research fields.

Målet med detta arbete har varit att undersöka möjligheten att utveckla och formulera en affärsidé och undersöka affärsmöjligheterna för den Taxi Service och app som Jimmy Wickström och Nico Ghatoore utvecklar som en del i deras kandidatexamensarbete. Detta har undersökts genom att utveckla affärsidéer och affärsmodeller tillsammans med Nico och Jimmy och sedan ta fram en detaljerad affärsplan för dessa och testa den teoretiskt och se huruvida den håller måttet och verkar rimlig. Undersökningen har visat att den har möjligheter att lyckas såvida de tre stora taxibolagen Taxi Stockholm, Taxi 020 och Taxi Kurir är villiga att köpa kunder för att fylla upp deras lediga kapacitet. / The goal of this project has been to investigate the possibility to start a business out of the Taxi-service and application Nico Ghatoore and Jimmy Wickström are developing as a part of their bachelor thesis. This goal has been reached by formulating business ideas and developing business models together with Nico and Jimmy and later written a detailed business plan according to the current standards and thus evaluating the quality of the business idea. This has shown us that it is possible to succeed given that the three largest taxi operators are willing to buy customers to fill upp their extra capacity.

Indiana University-Purdue University Indianapolis (IUPUI) / Medication nonadherence among middle-aged to older adults with chronic conditions often stems from forgetting to take or fill medications as prescribed. A pilot study indicated the feasibility of technology acceptance model (TAM)-based smartphone medication reminder app (SMRA) training as a way to promote their app use and medication adherence. This dissertation assesses the viability and effect size of the modified TAM-based SMRA training in promoting app use and medication adherence, as well as its delivery design in preparation for a larger efficacy study. A two-group pretest-posttest design was employed. Twenty-nine adults aged over 40 years and taking medications for chronic condition management were recruited from Midwestern university and community sites. The training group (n = 15) received the modified TAM-based SMRA training; whereas the non-training group (n = 14) self-navigated app features. The training group reported significantly higher levels of perceived usefulness, perceived ease of use, positive subjective norm, and intention to use the app. In addition, the training group reported a higher proportion of active app use than the non-training group. Modified TAM-based SMRA training was not viable in increasing the levels of medication adherence variables. Effect sizes suggested at least 52 participants as a sample size for a larger efficacy study. Participants suggested that training could be improved by scheduling separate group training for iPhone and Android phone users, providing a live online training option, providing small group training with peer helper, tailoring training length to participant preference, and working with family members and healthcare providers as co-trainees and co-trainers.