Enhancing your jogging experience with upgraded safeguard system.
Research, define, design, and prototype a mobile app that helps users who like listening to music while running automatically adjust their music volume according to the surroundings, historical data, and crowd sourcing data to keep outdoor running safe.
Yumei Jin, Zirui Wang, Yi Luo
Brainstorming ideas, User Interview, Survey, Usability Testing, Process book.
I was responsible for translating Chinese written materials in the research and design process into English.
Research and Design Process
We found supportive literatures from current research stating the emergent needs of Soundo.
Inattention Blindness Affects:
According to the research from the University of Maryland School of Medicine and the University of Maryland Medical Center in Baltimore, the number of pedestrians struck and seriously injured by cars or trains while wearing headphones has skyrocketed in recent years. Researchers reviewed 116 accident cases from 2004 to 2011 in which injured pedestrians were documented to be using headphones. Seventy percent of these accidents resulted in the pedestrian’s death. The majority of victims were male (68%) and under the age of 30 (67%). 55% of the moving vehicles involved in the accidents were trained, and nearly 29% of the vehicles reported sounding some type of warning horn prior to the crash. (http://eh- stoday.com/safety/news/safely-walk-listen-music-0117)
User Interview & Survey
After interviewing 3 users and conducting 56 surveys, we developed empathy with users by becoming more familiar with the problems they encountered while running.
Persona & User Journey Map
By analyzing the data from our user interviews and surveys, we drew valuable insights on users’ overall jogging experience and users’ needs. We built a persona and a user journey map to reflect our insights.
We analyzed 4 competitors that utilize sound detection technology to detect surrounding nosies and voices.
We identified 4 essential features through competitive analysis:
We decided to apply them onto Soundo. We also envision to incorporate the machine learning technology for sound classification to implement these features.
Key Findings From Research
- People have difficulties detecting surroundings when they are running with their headphones on, which gives rise to accidents.
- Listening to music while running is a must to some joggers because it helps them perform better.
- It is particularly necessary to lower down the music volume when:
- It comes to a crossroad;
- A vehicle is approaching.
- Users prefer to customize how they receive the alert of dangers.
- Data analysis and data visualization serves preventing potential dangers to joggers by providing in-time traffic status and running routes details.
Define & Ideation
Before we enter into prototype design and usability testing, we define Soundo as a sound detecting mobile application to enhance users' sensibility to ambient sounds while they are jogging with headphones on. Soundo alerts users by interrupting the current playing music when:
- Vehicles are approaching
- The user is arriving at the intersection.
Soundo applies machine learning technology to analyze and classify sounds of surroundings based on their frequencies.
Soundo also gives users the freedom to customize their favorite alerts by lowering music volumes and receiving voice messages. Plus, users are able to:
- Set preferred minimum volume and resume time;
- Review history data including alert frequencies, alert spots, and jogging routes log, to optimize future jogging experience in a safe way.
We envision Soundo’s essential functions as follows:
- Lower music volume;
- Send voice message;
- Record alerts;
- View previous alerts;
- Customize alerts.
prototype iterations with Usability Testing
We conducted 3 rounds of usability testing respectively with wireframe sketch, low fidelity prototype, and refined low fidelity prototype. For each round, we draw insights from key findings, and take the insights to improve our design of Soundo.
Round 1: Wireframe
Based on the big data of Soundo's community and the historical data of the user, Soundo automatically recommends safe jogging routes that best fits the user’s preferences. The user can set favorite alert mode by customizing settings in Soundo.
Thanks to Soundo's community, Soundo is able to predict the danger of the user’s unfamiliar area based on the data uploaded by other Soundo Users.
Summary of Key Findings from the 1st round of Usability Testing
- Users are satisfied with the main concept that the volume is automatically adjustable according to ambient sounds to alert users of dangerous surroundings.
- Users like the feature of customizing the alert methods.
- Users prefer to run alone for exercise.
- Users prefer to plan their routes by themselves. They only need route suggestions from Soundo based on their priori running experiences.
- Users do not think that the community will be useful because they only care about their own running experiences.
How the Key Findings Improved Our Design
- We add dashboard feature for users to review previous running experiences.
- We decided to abandon Soundo Community as it seems a redundant feature from the users feedback.
Round 2: Low-Fidelity Prototype
Summary of Key Findings from the 2nd round of Usability Testing
- Users are confused by too many functions, which means that the functions need to be simplified. Users expect the volume control and the danger alerts to be essential functions of the Soundo.
- Functions are not well categorized. Participants found it hard to find the right button to interact.
- Participants are confused about the buttons on home screen because the button names are not well representing the corresponding functions.
- Participants were confused about the Pause/Resume button on play screen and the delay adjusting bar on volume screen because functions seem to repeat each other.
How the Key Findings Improved Our Design
- We simplified the function types based on users feedback.
- We recategorized some essential functions.
- We modified the button names to better reflecting their functions.
Round 3: Refined Low-Fidelity Prototype
Summary of Key Findings from the 3rd round of Usability Testing
- Participants would like to have a feature to review their jogging history.
- Participants prefer darker theme color to prevent harsh light at night hurting their vison.
How the Key Findings Could Improve Our Future Design
- We could design a feature displaying users' jogging history.
- We could design a night mode UI for nighttime use.