Quantified Self

Summary

Intel has been exploring the personal data space for a few years now. One of those explorations is through Intel's involvement with the Quantified Self (QS) community. Qauntified Self is one of the projects I worked on at Intel, where I was part of a team that explored technology and design requirements that bring people and data together on one online platform for personal data analysis and sharing. My efforts on the project included the exploration of interaction and visual design concepts that would enable users to interact with, ask questions of, and explore patterns and correlations in their data.

Problem

Quantified Self (QS) is a grass roots movement and community of people interested in self-knowledge through self-tracking. With over 20,000 people (self-trackers) around the world, QS is defining the future of the personal data domain. A large variety of tools for personal data tracking and analysis have been introduced into the market; however, there remains a gap in tools for personal data analysis. Existing solutions for personal data analysis and visualization are either very complex (e.g. Matlab) or generalized and simplified (e.g. Fitbit, Runkeeper, and many other applications). The former present a technical impediment for a majority of users, the latter lack the flexibility and mechanism for users to freely explore questions about their data. As a step towards building a solution that fits the gap, interaction is leveraged in bridging the gap between under-the-hood data analysis and processing and user needs, intentions, and questions.

Process

The approach I took on this project was to first understand the bigger picture/goal and explore existing solutions. I spent some time refining and narrowing down to a very specifc and workable problem statement though back-and-forth secondary research and meetings with the rest of the team and team members. I also studied all the previous research material that had been conducted by the team. From this knowledge base, I then primarily focused on ideating interaction design concepts for bringing in data into the system and different mechanisms for analysis and making meaning of the data. I parallelly took part in design and other planning meetings, sought further immersion into the Quantified Self community by attending the Quantified Self Labs Google Hangounts and the Quantified Self conference, and used insights from these to iterate and build up the concept system.

At the QS 2013 Conference in San Francisco

Quantified Self Co-Lab Session Google Hangout

Product

Some of the results of the design process include:

Design Concept for Querying Data

I developed two distinct ways with which users can query/ask questions of their data: pattern exploration and data point exploration. I also developed the idea of quartile selection, which allows users to select data based on its quartile classification. With this, a user can easily select all data points that fit within each quarter (low, mid-low, mid-high, and high) of the quartile. This same concept can be used to divide data into 3 parts, which is a more intuitive method of division (low, mid, high).

Pattern Exploration

Quartile Querying

Design Concept for Aggregation & Personalization of Time

I developed two design concepts around aggregation: horizontal and horizontal aggregation. With horizontal aggregation, the user can see the data grouped into hours of the day, day of the week, week of the month, or month of the year. With vertical aggregation, the user can explore patterns in the data based on hours of the day, days of the week, weeks of the month, months of the year. I also developed a concept on the personalization of time that will enable users to group repeating time instances in each time level (e.g. hours, days, weeks, months). With this concept, the user can group certain hours together (e.g. morning, evening, work hours, etc.), certain days together (e.g. work days, weekends, exercise days, etc.), and so on. This allows the user to explore their data across these personalized meaningful time segments.

Horizontal Aggregation

Vertical Aggregation

Design Concept for Grouping Data

I developed a design concept for grouping data together, managing these groups, and using these groups to explore patterns and other analyses. This is a very promising novel area of exploration; however many technical and design questions arise as the user starts to

  1. group their raw and aggregated data based on shared temporal or value characteristics
  2. manage these groups
  3. analyze patterns and correlations in these groups

Grouping Data

Managing Groups

Exploring Patterns by Groups

Personal Data Tracking in Developing Africa

I had a chance to explore a personal research interest on the side during the internship period. I reached out locally to social scientists inside of Intel and globally to professors and other researchers outside of Intel to help guide this promising research topic in the greater HCI for Development domain. I also presented his research vision to Intel Foundation, on personal data tracking as a way to help girls and women achieve higher digital literacy and self-awareness—a mission of the foundation’s newly announced She Will Project.