Elf 신체환경웰니스 앱은

Press Kit

Unique Properties

Elf fosters the connection between environment health and body health by offering personalized suggestions about indoor environmental improvements and behavioral change. Using realtime environmental and health data, Elf coaches and nudges the user about actions they could take to their indoor environment to improve their everyday wellbeing. Through conversational UI and colors, the user is guided to understand key environmental qualities that are influencing how their body feels and performs.

Dimensions & Packaging

Elf is a mobile app for both iOS and Android users. It incorporates interactive and intuitive data visualization in showing environmental and body health data. The conversational UI supported by AI makes tips and advice easy to digest and helps align the character of the app to a house elf to create stronger emotional appeal. Machine learning algorithm calculates personal comfort zones based on body health profiles and environmental attributes to support personalization of advice.

Tags

Smart Environment, Wellbeing, Health, Interaction Design, Interface Design, Behavioral Change, Conversational UI, Data Visualization

Production Technology

The project is highly cross-disciplinary. We used lots of desktop research, expert interviews and data collection to gain deep domain knowledge around how key environmental metrics trigger +/- interactions with the human body indoors. We also conducted in-depth competitive analysis to find unique market positioning that intersects smart environment, wellbeing and habit-forming. We used human-centered design thinking to identify design principles, main features and data communication approach.

Design Challenge

1.Balance complexity of data &simplicity of communication. A related goal was to reduce mental load & ensure smooth experience for the most &least data-literate users. It is essential to communicate beyond numbers and we used gradient colors to convey overall qualities and conversational UI for further details. Together they also provide redundancy for enhanced accessibility. 2.Organize information hierarchy between data reading, tips and routines across the app to fit a natural mental model.

Research

Through domain expert interviews and in-depth literature research in environmental science and public health, we obtained deep domain knowledge that informed feature definition, feasibility and UX writing of the app. We collected data around environmental thresholds to calibrate the content and frequency of advice generation. We also interviewed 16 people of different profiles and data literacy levels, and the results led to defining the following: user personas, key use cases, and mental load perception preferences.

Inspiration

Studies have found that in North America and Europe, we spend90% of our time indoors and the quality of indoor environmental elements like air, sound and light affects comfort, wellbeing and productivity. While there are plenty of apps that monitor environmental and body data separately, less attention has been paid to connecting the two explicitly. How might we help people live healthier lifestyles by leveraging this connection and providing personalized coaching for their indoor health?

Project Duration

The project started Jan 2022 and completed this version in Sept 2022 in Cambridge, Massachusetts. It is an ongoing process with updates depending on new needs and business goals. It was selected into Harvard i-Lab’s Venture Incubation Program and was a Semi-Finalist at Harvard’s Presidential Innovation Challenge.

Operation Flow

Elf takes in environmental and body health data (tracking), identify areas of improvement (analysis), output suggestions (advice) and allows in-app sharing of information (community). Users start by setting up basic demographic profile, geolocation, health interests and data collection. The app has 4 main areas: home (see realtime environmental highlights, actionable tips and personal routine timeline), data (explore detailed data), tribe (share insights) and profile (bookmarks and settings).

Image Credits

Image #1: Tiange Wang, I-Yang Huang, 2022.
Image #2: Tiange Wang, 2022.
Image #3: Tiange Wang, 2022.
Image #4: Tiange Wang, 2022.
Image #5: Tiange Wang, 2022.