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Description
Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of

Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of health data, long term operation of wearable sensors and ensuring no harm to the user before actual marketing. Traditionally, clinical studies are used to validate the trustworthiness of medical systems. However, they can take long time and could potentially harm the user. Such evidences can be generated using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging.

This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes:

• Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment.

• Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology.

• Automatic code generation for MMAs: Investigating methods for automatically

• Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.
ContributorsBagade, Priyanka (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Wu, Carole-Jean (Committee member) / Doupe, Adam (Committee member) / Zhang, Yi (Committee member) / Arizona State University (Publisher)
Created2015
Description
Every engineer is responsible for completing a capstone project as a culmination of accredited university learning to demonstrate technical knowledge and enhance interpersonal skills, like teamwork, communication, time management, and problem solving. This project, with three or four engineers working together in a group, emphasizes not only the importance of

Every engineer is responsible for completing a capstone project as a culmination of accredited university learning to demonstrate technical knowledge and enhance interpersonal skills, like teamwork, communication, time management, and problem solving. This project, with three or four engineers working together in a group, emphasizes not only the importance of technical skills acquired through laboratory procedures and coursework, but the significance of soft skills as one transitions from a university to a professional workplace; it also enhances the understanding of an engineer's obligation to ethically improve society by harnessing technical knowledge to bring about change. The CC2541 Smart SensorTag is a device manufactured by Texas Instruments that focuses on the use of wireless sensors to create low energy applications, or apps; it is equipped with Bluetooth Smart, which enables it to communicate wirelessly with similar devices like smart phones and computers, assisting greatly in app development. The device contains six built-in sensors, which can be utilized to track and log personal data in real-time; these sensors include a gyroscope, accelerometer, humidifier, thermometer, barometer, and magnetometer. By combining the data obtained through the sensors with the ability to communicate wirelessly, the SensorTag can be used to develop apps in multiple fields, including fitness, recreation, health, safety, and more. Team SensorTag chose to focus on health and safety issues to complete its capstone project, creating applications intended for use by senior citizens who live alone or in assisted care homes. Using the SensorTag's ability to track multiple local variables, the team worked to collect data that verified the accuracy and quality of the sensors through repeated experimental trials. Once the sensors were tested, the team developed applications accessible via smart phones or computers to trigger an alarm and send an alert via vibration, e-mail, or Tweet if the SensorTag detects a fall. The fall detection service utilizes the accelerometer and gyroscope sensors with the hope that such a system will prevent severe injuries among the elderly, allow them to function more independently, and improve their quality of life, which is the obligation of engineers to better through their work.
ContributorsMartin, Katherine Julia (Author) / Thornton, Trevor (Thesis director) / Goryll, Michael (Committee member) / Electrical Engineering Program (Contributor) / School of Film, Dance and Theatre (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12