Mobile App Design for Supporting Shared Use of Personal Sensing Data between Long Hour Gig Workers and Intimate Partners
|Project title||Mobile App Design for Supporting Shared Use of Personal Sensing Data between Long Hour Gig Workers and Intimate Partners|
|Background||Flexible gig work, i.e., non-traditional one consisting of income-earning activities outside of traditional, long-term employer-employee relationships, is growing rapidly. Although workers of this kind (e.g., Uber and Lyft drivers) have more flexibility on deciding whether or not as well as when to accept a job, they also tend to increase their amount of working by accepting more jobs (i.e., turning to long-hour workers) to earn more income despite the fact that they seemed to be given greater control over accepting a job. It is often because low hourly wage is associated with each piece of work performed. Through our pilot user research done with Taxi and Uber drivers recently, we’ve learned that these workers don’t necessarily have a situated awareness and appropriate understanding on how their work impact well-being, neither do their intimate partners.|
|Description||To promote workers’ wellbeing and to achieve better life-work balance among the target group, we will design a mobile app for supporting socially shared viewing and use of personal sensing data provided by wearable health-monitoring sensors (e.g., Fitbit, heart rate bands etc.). Going beyond individual viewing of these data by workers themselves, in this senior project, personal sensing data will be made accessible to a worker’s ego social network, such as the worker’s spouse/partner, family members and/or close friends. It is expected that socially shared use of personal data can provide a safety net conducive to the wellbeing of the workers. But at the same time, challenges related to inconsistent interpretations of sensing data as well as privacy would also arise, and need to be considered as part of system design.
The project team will design and build a “Socially Supported Wellbeing” App for Android devices based on insights obtained from our past user research done on Taxi and Uber drivers in Taiwan, and well as new qualitative data we’ll be collecting on Uber and Lyft drivers locally surrounding the Davis area. The project team will engage students in a complete HCI design process, going through the entire cycle of human-centered design, including:
● Understanding users - observing and interviewing Uber and Lyft drivers in Davis, Sacramento, and San Francisco areas to understand the general states and common issues of worker’s well-being.
● Sketching and on-paper prototyping - using non-programming tools to determine the functions, features, looks and trade-offs associated with the app design. Generating alternative designs for internal discussion and testing, as well as the formulation of a design milestone.
● Functional prototyping - Development of a workable app to deliver the features and functions selected to implement using Android and APIs provided by Fitbit and other sensors.
● Testing and evaluation in the lab environment and/or in the field.
|Deliverable||● A working prototype of the Social Support App for long hour workers for a group of faculty and student researchers working on this issue.
● The students will be expected to deliver the App’s source code in GitHub
● Documentation of the code is required
● Documentation of the design and development process, including a concise report of
user observation and user interviews, as well as a concise
● Learning outcomes and benefits to students - obtaining the experience and know-how in
delivering a product-like system design as outlined.
|Skill set desirable||● An ideal time will have interest and experience in working on Android app development, IoT sensors like Fitbit, pedometers and heart rate bands.
● Basic skills in web and database programming
● Knowledge about web frameworks, Restful API, Node.js and MongoDB are definitely a
● At least one team member needs to possess interest and/or experience in user studies,
which can be qualitative interview studies or quantitative lab studies and statistical analysis.
|Client time availability||30-60 min every two weeks|
|IP requirement||Open source project|